# Machine Learning on Blockchain Data: A Systematic Mapping Study

Georgios Palaiokrassas1,2, Sarah Bouraga3, Leandros Tassiulas1,2

1Yale Institute for Network Science, Yale University, USA
2Department of Electrical Engineering, Yale University, USA
3Namur Digital Institute (NADI), Belgium
{georgios.palaiokrassas<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

Context: Blockchain technology has drawn growing attention in the literature
and in practice. Blockchain technology generates considerable amounts of data
and has thus been a topic of interest for Machine Learning (ML).

Objective: The objective of this paper is to provide a comprehensive review of
the state of the art on machine learning applied to blockchain data. This work
aims to systematically identify, analyze, and classify the literature on ML
applied to blockchain data. This will allow us to discover the fields where
more effort should be placed in future research.

Method: A systematic mapping study has been conducted to identify the relevant
literature. Ultimately, 159 articles were selected and classified according to
various dimensions, specifically, the domain use case, the blockchain, the
data, and the machine learning models.

Results: The majority of the papers (49.7%) fall within the Anomaly use case.
Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset
consisting of more than 1.000.000 data points was used by 31.4% of the papers.
And Classification (46.5%) was the ML task most applied to blockchain data.

Conclusion: The results confirm that ML applied to blockchain data is a
relevant and a growing topic of interest both in the literature and in
practice. Nevertheless, some open challenges and gaps remain, which can lead
to future research directions. Specifically, we identify novel machine
learning algorithms, the lack of a standardization framework, blockchain
scalability issues and cross-chain interactions as areas worth exploring in
the future.

Keywords: Blockchain, Machine learning, Systematic mapping study

##  1 Introduction

Blockchain technology has sparked a lot of interest over the years. A
particularly interesting characteristic of the technology is the transparency
it offers. Indeed, all the transactions recorded on a public blockchain
(amounting to hundreds of thousands of transactions a day, just for Bitcoin)
can be viewed, retrieved and analyzed by anyone. This is a huge paradigm
shift, compared to incumbent institutions such as traditional banks.

The amount of available blockchain data offers a lot of potential for
analysis. We can analyze the blockchain data to discover unknown patterns in
the data, or to predict the next cryptocurrency price, or to detect fraud to
name a few. In order to carry out these types of analyses, we can use machine
learning, a subfield of Artificial Intelligence. Since machine learning
requires a lot of data to perform well, and since blockchain data are public
and available in large quantities, this seems like a match made in heaven.

This claim is supported by the plethora of scientific articles we analyze
here. Many researchers addressed the questions raised above, i.e. they tried
to discover hidden patterns in blockchain data, they proposed solutions for
the prediction of cryptocurrency prices, others focused on the detection of
fraudulent activity on a blockchain, and multiple other use cases.

Due to the rapid evolution of both technologies (blockchain and machine
learning), it is not trivial to keep track of the state of the art: What has
been done? How? On which platform?… We believe it is essential for researchers
and practitioners to have a clear view of the current state of the art. On the
one hand, practitioners need to know the new solutions for a given problem,
taking advantage of the latest advances in a given field. Researchers, on the
other hand, need to know the works they can build on and the research
directions they can pursue.

Hence, the aim and corresponding contribution of this paper is to propose a
Systematic Mapping Study of scientific papers applying machine learning to
blockchain data. As explained in Section 3, we apply the rigorous methodology
and follow the guidelines proposed by [1, 2]. Specifically, our contribution
is fourfold:

  1. 1.

We identify, analyze and organize 159 papers, published between 2008 and 2023,
applying machine learning to blockchain data. Section 4 shows the distribution
of the papers across venues (Book chapter, Conference proceedings, and journal
articles) and their evolution throughout the years.

  2. 2.

Using keywording, we propose a classification scheme organizing the studies
across multiple dimensions, namely: the Use Case, the Blockchain, the Data,
and the Machine Learning task. We present the classification scheme in Section
5.

  3. 3.

We propose a mapping of the studies, focusing on the use cases: Address
classification, Anomaly detection, Cryptocurrency price prediction,
Performance prediction, and Smart contract vulnerability detection. We present
the mapping results in Section 6.

  4. 4.

We identify potential research gaps in Section 7, where we also discuss the
results of this work.

The main aim of this work is to investigate the current state of the art
regarding machine learning on blockchain data. Specifically, we aim to assess
the academic publication aspect, such as the popular forums and publication
types; as well as the technical aspects, such as the blockchains that have
been analyzed, the datasets used and/or curated by researchers, and the
machine learning algorithms that were applied. We hope the study will provide
the reader with a clear overview of the current research of machine learning
on blockchain data, and that it will highlight potential research gaps,
suggesting potential future research directions for researchers.

###  1.1 Related Surveys

Various authors proposed related surveys, which we classified into three
categories: (i) general surveys about blockchain and machine learning, (ii)
surveys about blockchain and machine learning in a specific sector/industry or
application, and (iii) systematic reviews.

Some researchers proposed an overview of blockchain technology and of
artificial intelligence applications in the blockchain. Siddiqui & Haroon [3]
introduced blockchain technology, its characteristics and benefits; then
explored how AI and blockchain can be combined in order to mitigate their
limitations. The authors also offered a wide range of examples of applications
of these two technologies; and discussed the case of edge computing.
Similarly, Inbaraj & Chaitanya [4] provided an overview of blockchain and AI,
discussed the implementation of blockchain in various industries, and
discussed the integration of blockchain and AI in various applications.

Other works focused on blockchain and artificial intelligence in a specific
sector or for a specific application. A number of authors proposed an analysis
of existing works addressing blockchain and machine learning related to the
Internet of Things (IoT). Aoun et al. [5] proposed an overview of IoT, its
challenges and how blockchain technology can help in the development of the
Industry 4.0. In [6], the authors provided a summary and analysis of such
works using three perspectives, namely: consensus mechanism, storage, and
communication. The authors discussed how blockchain and machine learning
interact in Industrial IoT (IIoT) and highlight the security and privacy risks
of such solutions. Liu et al. [7] analyzed works exploring blockchain-enabled
federated learning in the context of Digital Twin, from aspects pertaining to
security, fault-tolerance, fairness, efficiency, cost-saving, profitability,
and support for heterogeneity. Various authors addressed the issue of IoT
security. On the one hand, Williams et al. [8] reviewed the security in IoT
and emphasized the impact of emerging technologies, including fog/edge/cloud
computing and quantum computing in addition to blockchain and machine
learning. On the other hand, in [9], the authors analyzed how machine
learning, artificial intelligence, and blockchain technology can help to
address IoT security. In [10], the authors discussed how we can integrate
blockchain technology and artificial intelligence with smart grids in order to
facilitate prosumers’ participation in energy markets.

Han et al. [11] adopted the same approach but applied it to the case of
accounting and auditing. Cheng et al. [12] reported on the use of machine
learning and blockchain technology in the healthcare sector, and more
specifically discussed the implications for cancer care.

A study closer to our research here would be [13], where the authors analyzed
works related to cryptocurrency research and machine learning. The findings of
this work: (i) confirm the popularity of the topic in research; (ii) show that
cryptocurrency price (or related) prediction is the most popular topic; and
(iii) indicate that many different algorithms are used and that common
problems such as overfitting and interpretability are still present.

Mainly two elements differentiate our work here from these related surveys.
Firstly, we do not focus on a specific area, but provide an overview of
existing works pertaining to a wide range of industries, sectors, and
applications. In particular, we do not restrict our analysis to cryptocurrency
as in [13] but our review encompasses works related to a wide range of use
cases. Secondly, we focus on machine learning on blockchain data; while many
of the mentioned surveys addressed works about the integration of the two
technologies to a specific problem or opportunity.

Finally, we identified a bibliometric analysis, two systematic reviews, and a
survey. Bai & Sarkis [14] conducted a bibliometric and network analysis to
identify research areas related to formal analytical modeling for blockchain
in the domain of supply chains. In [15], the authors performed a systematic
review of the use of blockchain management and machine learning in the
particular context of IoT environment. Lin et al. [16] carried out a
systematic review and analyzed 25 papers treating unsupervised learning,
supervised learning and topological analysis for the detection of illicit
transactions on the Bitcoin blockchain. Finally, a comprehensive survey was
conducted in [17], where the authors depicted the state of the art regarding
anomaly detection in blockchain networks. They analyzed papers and classified
them based on the blockchain layers, namely, the data layer, the network
layer, the incentive layer, and the smart contract layer.

We believe our work is different from the mentioned surveys and hence adds
value to the literature. Indeed, the scope we consider here is broader than
the ones addressed in [14, 15, 16, 17]. We propose a coarse-grained analysis
of the state of the art regarding machine learning applied to blockchain data.
We do not focus here on a particular use case or industry. In addition,
conducting our review now allows us to consider recently published papers that
could not have been treated by the previous surveys. Finally, for the coarse-
grained analysis, we propose different classification dimensions than the ones
discussed in previous surveys. The current work complements thus the existing
surveys proposed by fellow researchers.

##  2 Background

As stated above, the availability of large volumes of blockchain data offers a
great opportunity for analysis. Classical machine learning tasks - such as
hidden patterns, trends, or outliers detection - can be performed on these
data. In the following subsections, we offer a background on blockchain
technology and on machine learning.

###  2.1 Blockchain

Blockchain was originally proposed in 2008 as the accounting method for
Bitcoin cryptocurrency [18]. The technology and ideas evolved in the years
that followed and many blockchains and altcoins were introduced. A milestone
for the course of blockchain technology was the introduction of Ethereum in
2015 [19], an open decentralized blockchain platform which provides a virtual
computing environment called Ethereum Virtual Machine, but also a Turing
complete programming language to write smart contracts. Ethereum smart
contracts are actually program instances running on the decentralized network
and any user is allowed to deploy smart contracts enabling the development of
different Decentralized Applications (DApps) with potential for different
fields such as IoT [20], copyright management [21], supply chain management
[22], healthcare [23], energy [24], Decentralized Finance (DeFi) [25] and many
more.

Blockchain is actually a distributed ledger or distributed database recording
digital transactions between two parties without the need for Third Trusted
Parties (TTP). This allows the interaction of users without an intermediary,
while anonymity is preserved, as one of the key blockchain’s features. Every
participating node in this peer-to-peer network has an actual ”copy” of all
the transactions that took place, ensuring their immutability and enhancing
security. Security is also preserved by the utilization of cryptography, which
is one of the principal aspects of blockchain technology.

The transactions within a ledger are verified by multiple nodes or
“validators,” within the cryptocurrency’s peer-to-peer network using one of
many varied consensus algorithms for resolving the problem of reliability in a
network involving multiple unreliable nodes. Different consensus mechanisms
have been proposed allowing the network of nodes to agree on the state of a
blockchain. The most widely used consensus algorithms are the Proof of Work
(PoW) algorithm and the Proof of Stake (PoS) algorithm [26]. Bitcoin for
example uses a PoW-based consensus protocol, while Ethereum transitioned from
a PoW to PoS-based consensus protocol. However, there are also other consensus
algorithms, which utilize alternative implementations of PoW and PoS, as well
as other hybrid implementations and some altogether new consensus strategies.

An increasing amount of blockchain data is generated through various
interactions and activities. The type of data varies depending on the type of
blockchains, the supported functionalities and remains unchanged given the
immutable nature of ledgers. Blockchain data consists mainly of transaction
and block data, smart contract logs, events, and interactions, network
activity, and topology, while data analysis and machine learning techniques
have been applied to them combining often external datasets such as
cryptocurrency prices, news feed and public sentiment.

###  2.2 Machine Learning

As stated by M. Jordan and T. Mitchell, Machine Learning addresses the
question of how to build computers that improve automatically through
experience. It is one of today’s most rapidly growing technical fields, lying
at the intersection of computer science and statistics, and at the core of
artificial intelligence and data science [27]. According to another definition
by Deisenroth et al. [28], Machine learning is about designing algorithms that
automatically extract valuable information from data with emphasis on
“automatic”, i.e., machine learning is concerned about general-purpose
methodologies that can be applied to many datasets, while producing something
that is meaningful.

A learning problem can be defined as the problem of improving some measure of
performance when executing some task, through some type of training
experience. For example, in our study we identified several works attempting
to learn to detect fraud in blockchain transactions, where the task is to
assign a label of “fraud” or “not fraud” to any given blockchain transaction.
The performance metric to be improved might be the accuracy of this fraud
classifier, and the training experience might consist of a collection of
historical transactions, each labeled in retrospect as fraudulent or not.

A typical workflow of an ML framework commonly consists of the training phase
and the testing phase, while sometimes the validation phase is part of the
flow [29]. It could be noted that other steps are involved in some other
categories of tasks and methods such as Reinforcement Learning or Federated
Learning. In general, ML techniques can be classified into four different
areas:

(i) Supervised learning where the learning algorithm learns from labeled data.
The training data take the form of a collection of (x, y) pairs and the goal
is to produce a prediction y* in response to a query x*,

(ii) Unsupervised learning generally involves the analysis of unlabeled data
under assumptions about structural properties of the data.

(iii) Semi-supervised learning is the branch of machine learning concerned
with using labeled as well as unlabeled data to perform certain learning
tasks. Conceptually situated between supervised and unsupervised learning, it
permits harnessing the large amounts of unlabeled data available in many use
cases in combination with typically smaller sets of labeled data [30].

(iv) Reinforcement learning where the information available in the training
data is intermediate between supervised and unsupervised learning. Instead of
training examples that indicate the correct output for a given input, the
training data in reinforcement learning are assumed to provide only an
indication as to whether an action is correct or not.

Regardless of the area or the task, there are some steps appearing in the
workflows of the different ML solutions and research efforts. The most common
ones are: i) Data Collection; (ii) Data Preprocessing; (iii) Feature
Extraction and (iv) Algorithm Selection.

##  3 Methodology

Following [31], we conducted this systematic mapping study by considering both
the guidelines proposed by [2] and by [1]. In this section, we detail the
steps we performed in order to carry out our study.

###  3.1 The Research Questions

Systematic mapping studies, in general, aim to provide an overview of the
current research in a given area [2]. In this study, we focus on blockchain
and machine learning; and we offer a coarse-grained analysis of the current
research, we identify the quantity and type of research, and the gaps and
future research directions. Specifically, the overarching goal can be defined
by the following research questions:

  * •

RQ1. Which topics related to machine learning on blockchain have been
investigated and to what extent?

  * •

RQ2. What diverse types of blockchain data have been analyzed and to what
extent has each type been represented?

  * •

RQ3. Which machine learning types of models have been applied on blockchain
data and to what extent has each type of model been represented?

  * •

RQ4. In which forums has research on machine learning on blockchain been
published?

Answering these research questions will give us a comprehensive overview of
the current state of research, and thereby addressing the main goal of this
paper.

###  3.2 Data Sources and Search Strategy

The database sources that have been used as primary sources in this study are
the following:

  * •

Google Scholar: https://scholar.google.com

  * •

Springer: https://link.springer.com

  * •

ScienceDirect: https://www.sciencedirect.com

These are some of the most commonly used database sources in software
engineering. We started the study in December 2022 and, given the relative
novelty of the technology, we decided to search publications without any
period limitation.

The first step was to define search keywords as recommended by [1]. We
considered the terms “Blockchain”, “Smart contract”, “Decentralized
application”, “Bitcoin”, “Ethereum”, “Machine learning”, “Analytics”, and
“Artificial intelligence” as our keywords. We used the logical operators OR
and AND to link the main keywords. For some database sources, we conducted
several searches to cover all keywords. Specifically, we started for instance
with (Blockchain AND “Machine learning”), retrieved the returned publications,
and repeated the search with the other keywords combinations. For other
database sources, we used the following single search string:

  * •

ALL((blockchain or “DApp” or ”Ethereum” or “Bitcoin” or “smart contract” or
”decentralized application”) AND (“machine learning” or “analytics” or
”artificial intelligence” or ”ai”))

The search strings were applied to search the database sources by considering
the title, abstract and keywords.

###  3.3 Study Selection

We developed inclusion and exclusion criteria in order to select the most
relevant and important publications. We confronted the title, abstract and
full text of the articles previously retrieved, in order to ensure that the
publications fit the scope of our study.

We excluded studies that exclusively provided a discussion or a conceptual
solution of blockchain and machine learning (as in [32, 33, 34, 35]). The
reason for this exclusion is that we are interested in the data and models
used to address a specific problem. Secondly, we are interested in the
application of machine learning on blockchain data. Hence, the papers
addressing the use of blockchain technology to enhance the performance of
machine learning were not included (such as [36, 37]). Similarly, publications
analyzing data without any features pertaining to blockchain data were
excluded, for instance cryptocurrency price prediction that only use data
about the historical prices (as in [38, 39, 40, 41, 42, 43, 44]) or that only
use traffic data [45, 46]. Furthermore, articles where no machine learning
model was applied to the data were also excluded, for instance articles
providing a descriptive statistics analysis (such as [47, 48]). Finally,
surveys were also excluded since they do not provide a new machine learning
solution but discuss and analyze existing ones [13, 3, 4].

To summarize, the inclusion and exclusion criteria applied in this study are
the following.

The inclusion criteria:

  * •

Articles must report on the application of machine learning to blockchain data

  * •

Articles must be peer-reviewed

  * •

Articles must be written in English

  * •

Articles are published in or after 2008

  * •

Articles must be published

The exclusion criteria:

  * •

Articles propose a discussion or a conceptual solution of blockchain and
machine learning, such as surveys

  * •

Articles are not written in English

  * •

Articles are not peer-reviewed

  * •

Articles are published before 2008

  * •

Articles are on pre-press

The selection of the primary studies was done in four phases and was conducted
by two reviewers who examined all the retrieved studies. For each study, there
was a data extractor. The data extraction form was then checked by the other
reviewer. If any conflicts were raised, the authors resolved them by examining
the paper together. The four phases of the selection are the following:

  1. 1.

Phase 0. Application of search strings to database sources \- Number of papers
included in the next phase: 3434. The total number of articles retrieved from
the database sources (Springer, ScienceDirect and Google Scholar) was 3434.
The results of these articles were included in the next phase.

  2. 2.

Phase 1. Title-based selection. \- Number of papers included in the next
phase: 376. In this phase, we read and assessed the paper’s title using our
inclusion and exclusion criteria. If the paper fit the scope of the study, it
was included in the next phase. Otherwise, it was discarded. When the
evaluation of the title was difficult and/or led to debate, we decided to
include the paper in the next phase.

  3. 3.

Phase 2. Removal of duplicates. \- Number of papers included in the next
phase: 304. We found 72 duplicates, we removed them from our data and we
included the remaining 304 papers in the next phase.

  4. 4.

Phase 3. Abstract-based selection. \- Number of papers included in the next
phase: 131. We read the abstract and keywords of each paper. It allowed us to
confirm or infirm their selection and inclusion in the next phase. We
discarded 173 papers in this phase and included the remaining 131 in the next
phase.

  5. 5.

Phase 4. Full text selection. \- Number of papers included in the next phase:
85. In this phase, we read the full text of the 131 selected papers from phase
3. This allowed us to make the final selection and make sure that the articles
included in the study were actually related to the study. Using our inclusion
and exclusion criteria, we further discarded 46 articles. This resulted in a
final set of primary studies consisting of 86 articles. We recorded the basic
information of each of these papers in an Excel sheet, namely: the title, the
authors, the publication type and the year of publication.

  6. 6.

Phase 5. Secondary search. \- Number of papers included: 159. In this phase,
we conducted an additional selection of papers based on the references of the
articles included in Phase 4. This allowed us to include papers that we may
have missed in earlier phases. We proceeded in the same way as above.
Specifically, we first selected the papers based on the title, and we removed
the duplicates. We then read the abstract, the keywords, and the full text. In
order to be included in the final set of studies, the papers had to satisfy
the inclusion and exclusion criteria. This phase allowed us to include 74
additional papers, which resulted in a final set of studies consisting of 159
articles.

This process is summarized in Figure 1.

Figure 1: Selection Process

##  4 Studies

The studies selected in this systematic mapping study consisted of 159
articles taking the form of journal articles, conference proceedings, book
chapters and workshop proceedings from 2015 to 2023. In order to address RQ4,
we identified the publication years and forums of the studies. Tables 1 and 2
show a summary of the publication forums of the studies, and the distribution
of the studies by publication type respectively. We can see that the IEEE
Access, Expert Systems with Applications, and the International Conference on
Blockchain and Trustworthy Systems are the most popular forums; and that 2022
and 2021 were the years where most research publications were proposed.

Table 1: Publication Forums of Primary Studies Publication Source |  Count of papers
---|---
Book chapter |  1
Blockchain Intelligence |  1
Conference proceedings |  82
International Conference on Blockchain and Trustworthy Systems |  7
International Conference on Network and System Security |  3
Hawaii International Conference on System Sciences |  2
IEEE International Conference on Blockchain |  2
IEEE International Conference on Blockchain and Cryptocurrency (ICBC) |  2
IEEE International Symposium on Circuits and Systems (ISCAS) |  2
International Conference on Complex Networks and Their Applications |  2
ACM International Conference on AI in Finance (ICAIF) |  1
ACSAC: Annual Computer Security Applications Conference |  1
Advances in Knowledge Discovery and Data Mining |  1
Biometric and surveillance technology for human and activity identification XII |  1
Blockchain and Trustworthy Systems |  1
Blockchain Research and Applications for Innovative Networks and Services (BRAINS) |  1
CAAI International Conference on Artificial Intelligence |  1
Crypto Valley Conference on Blockchain Technology (CVCBT) |  1
IEEE Annual Computers, Software, and Applications Conference (COMPSAC) |  1
IEEE Confs on Internet of Things, Green Computing and Communications, Cyber, Physical and Social IEEE Global Communications Conference |  1
IEEE Global Engineering Education Conference |  1
IEEE Int Conf on High Performance Computing & Communications; Int Conf on Data Science & Systems; Int Conf on Smart City; Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) |  1
IEEE International Conference on Big Data (Big Data) |  1
IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) |  1
IEEE International Conference on Machine Learning and Applications |  1
IEEE International Conference on Software Quality, Reliability and Security |  1
IEEE Symposium Series on Computational Intelligence |  1
Information Science and Applications |  1
Information Security for South Africa |  1
Information Systems in a Changing Economy and Society |  1
Intelligent Computing and Innovation on Data Science |  1
International AAAI Conference on Web and Social Media |  1
International Conference of Smart Systems and Emerging Technologies (SMARTTECH) |  1
International Conference on Advanced Communication Technology |  1
International Conference on Advanced Data Mining and Applications |  1
International Conference on Advances in Cyber Security |  1
International Conference on Algorithms and Architectures for Parallel Processing |  1
International Conference on Automated Software Engineering |  1
International Conference on Big Data, Information and Computer Network (BDICN) |  1
International Conference on Big Data and Security |  1
International Conference on Blockchain Computing and Applications (BCCA) |  1
International Conference on Computer Communication and Networks |  1
International Conference on Computer Theory and Applications (ICCTA) |  1
International Conference on Data Mining Workshops |  1
International Conference on Data Science and Computer Application (ICDSCA) |  1
International Conference on Deep Learning, Big Data and Blockchain |  1
International Conference on Digital Forensics |  1
International Conference on Information Security Theory and Practice |  1
International Conference on Information Technology (ICIT) |  1
International Conference on Information Technology and Quantitative Management |  1
International Conference on Intelligence and Security Informatics |  1
International Conference on Internet of Things: Systems, Management and Security |  1
International Conference on Internet Measurement |  1
International Conference on Machine Learning Technologies (ICMLT) |  1
International Conference on Mobile Ad Hoc and Smart Systems (MASS) |  1
International Conference on Mobile Networks and Management |  1
International Conference on Parallel, Distributed, and Network-Based Processing |  1
International Conference on Privacy, Security and Trust |  1
International Conference on Service-Oriented System Engineering |  1
International Conference on Smart City Applications |  1
International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO) |  1
International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |  1
International Conference on Ubiquitous and Future Networks (ICUFN) |  1
International Conference Web Information Systems Engineering |  1
International Congress on Blockchain and Applications |  1
International Joint Conference on Neural Networks |  1
International Symposium on Software Reliability Engineering |  1
Italian Conference on CyberSecurity |  1
Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining |  1
Recent Trends in Analysis of Images, Social Networks and Texts |  1
World Wide Web Conference |  1
Journal |  73
IEEE Access |  5
Expert Systems with Applications |  4
MDPI Sensors |  3
Applied Soft Computing |  2
Blockchain: Research and Applications |  2
IEEE Transactions on Computational Social Systems |  2
IEEE Transactions on Systems, Man, and Cybernetics: Systems |  2
IEEE Transactions on Network Science and Engineering |  2
Information Processing and Management |  2
Mathematics |  2
MDPI Electronics |  2
PloS one |  2
Academy of Accounting and Financial Studies Journal |  1
ACM SIGMETRICS Performance Evaluation Review |  1
ACM Transactions on Internet Technology |  1
Applied Intelligence |  1
CCF Transactions on Pervasive Computing and Interaction |  1
Computational Economics |  1
Computer Communications |  1
Computer Networks |  1
Data & Knowledge Engineering |  1
Data Science and Management |  1
Decision Support Systems |  1
EPJ Data Science |  1
Eurasian Economic Review |  1
Finance Research Letters |  1
Future Generation Computer Systems |  1
IEEE Systems Journal |  1
IEEE Transactions on Circuits and Systems II: Express Briefs |  1
IEEE Transactions on Dependable and Secure Computing |  1
IEEE Transactions on Information Forensics and Security |  1
International Journal of Information Technology |  1
International Journal of Forecasting |  1
International Journal of Information Security |  1
IOP SciNotes |  1
Journal of Behavioral and Experimental Finance |  1
Journal of Combinatorial Optimization |  1
Journal of Computational and Applied Mathematics |  1
Journal of Finance and Data Science |  1
Journal of Management Information Systems |  1
Journal of Risk and Financial Management |  1
Journal of Supercomputing |  1
Journal of Systems Architecture |  1
Knowledge-Based Systems |  1
Measurement: Sensors |  1
Multimedia Tools and Applications |  1
Neural computing and applications |  1
Neural Processing Letters |  1
North American Journal of Economics and Finance |  1
Pattern Recognition Letters |  1
Peer-to-Peer Networking and Applications |  1
Physica A |  1
Science China Information Sciences |  1
Security and Communication Networks |  1
SN Computer Science |  1
Workshop proceedings |  3
International Workshop on Emerging Trends in Software Engineering for Blockchain |  1
International Workhsop on Financial Cryptography and Data Security |  1
International Workshops of ECML PKDD on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |  1
Total |  159
Table 2: Distribution of Studies by Publication Type. Publication Type |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Book Chapter |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  0 |  0 |  1
Conference Proceedings |  1 |  0 |  4 |  2 |  3 |  12 |  14 |  17 |  19 |  9 |  1 |  82
Journal |  0 |  0 |  1 |  0 |  2 |  3 |  6 |  13 |  16 |  26 |  6 |  73
Workshop Proceedings |  0 |  0 |  0 |  0 |  1 |  0 |  1 |  0 |  0 |  1 |  0 |  3
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159

##  5 Classification Scheme and Distribution Trend

Following [2], we designed our classification scheme by keywording.
Specifically, we identified relevant keywords by reading abstract and this set
of keywords allowed us to define a number of dimensions that we grouped into
high level categories in order to form a structured framework. We should note
that the list of keywords evolved with our reading, i.e. new keywords were
added to the list as we analyzed new publications.

The studies were classified from four perspectives: (i) use case, (ii)
blockchain, (iii) data, and (iv) machine learning. We should note that it is
common to classify studies in software engineering based on the contribution
type and the research type [2]. However, given the focus of this study, we
elected not to use these dimensions. Indeed, we selected papers applying
machine learning to blockchain data in order to fulfill a particular goal.
Hence, most studies provide a “Method” contribution (Contribution type) and
propose a “Solution” (Research type). We believe that incorporating these
dimensions in our analysis would not have given the reader a lot of valuable
insight.

With the classification scheme clearly defined, we sorted the relevant
articles into this scheme. We used an Excel table to document the data
extraction process. The table contained each category of the classification
scheme. As mentioned earlier, only one reviewer filled the data extraction
form (i.e. a row in our Excel table) and the other reviewer checked the form
afterwards. Conflicts were resolved by analyzing and discussing the paper
together. Once all conflicts were resolved, we computed the frequencies of
publications for each category.

###  5.1 Use Case and Distribution

Using the keywording technique described in [2], we identified five major use
cases:

  * •

Address Classification. While pseudo-anonymity is a major property of
blockchains, studies have addressed the de-anonymization of blockchain users.
This use case pertains to user identification, address classification, address
clustering, etc.

  * •

Anomaly Detection. This use case relates to the detection of any suspicious
behavior on a blockchain, e.g. Ponzi Scheme detection, Illicit transaction
detection, Attack detection, etc.

  * •

Cryptocurrency Price Prediction. This use case pertains to the prediction of
cryptocurrency price, e.g. Bitcoin, Ether, etc.

  * •

Performance Prediction. Scalability is an important challenge for the
blockchain community. This use case pertains to the prediction of performance,
such as the prediction of: transaction throughput, transaction confirmation
time, transaction fees, etc.

  * •

Smart Contract Vulnerability Detection. Given the immutability property of
blockchains, vulnerability in smart contracts and DApps can have significant
and dangerous consequences. Hence, studies have addressed the evaluation and
detection of vulnerability in smart contracts.

Table 3 shows a summary of the distribution of the selected studies by use
case.

Table 3: Distribution of Studies by Use Case. Use Case |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Address Classification |  1 |  0 |  1 |  0 |  2 |  9 |  4 |  3 |  4 |  4 |  0 |  28
Anomaly Detection |  0 |  0 |  1 |  2 |  1 |  2 |  8 |  17 |  22 |  20 |  6 |  79
Price Prediction |  0 |  0 |  3 |  0 |  1 |  4 |  5 |  5 |  4 |  7 |  1 |  30
Performance Prediction |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  1 |  3 |  2 |  0 |  7
Vulnerability Detection |  0 |  0 |  0 |  0 |  0 |  0 |  3 |  4 |  3 |  2 |  0 |  12
Other |  0 |  0 |  0 |  0 |  2 |  0 |  0 |  0 |  0 |  1 |  0 |  3
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159

###  5.2 Blockchain and Distribution

The studies applied machine learning to data generated by various blockchains:

  * •

Bitcoin. The blockchain data analyzed in the paper were coming solely from the
Bitcoin blockchain.

  * •

Ethereum. The blockchain data analyzed in the paper were coming solely from
the Ethereum blockchain.

  * •

Bitcoin and Ethereum. The blockchain data analyzed in the paper were coming
from both the Bitcoin and Ethereum blockchain.

  * •

Multiple. The blockchain data analyzed in the paper were coming from multiple
blockchains (other than the combination of Bitcoin and Ethereum).

Table 4 shows a summary of the distribution of the selected studies by
blockchain.

Table 4: Distribution of Studies by Blockchain. Blockchain |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Bitcoin |  1 |  0 |  5 |  2 |  4 |  13 |  12 |  13 |  11 |  12 |  2 |  75
Ethereum |  0 |  0 |  0 |  0 |  1 |  1 |  9 |  16 |  17 |  19 |  5 |  68
Bitcoin and Ethereum |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  3 |  2 |  0 |  6
Multiple |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  2 |  1 |  0 |  3
Other |  0 |  0 |  0 |  0 |  1 |  1 |  0 |  0 |  3 |  2 |  0 |  7
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159

###  5.3 Data and Distribution

The studies in this paper used different data sources:

  * •

Blockchain. The authors extracted the data directly from the blockchain they
studied, whether it is Bitcoin or Ethereum.

  * •

Website. The authors extracted the data from a blockchain explorer website,
such as blockchain.com, etherscan.io, walletexplorer.com, or xblock.pro.

  * •

Published Dataset. The authors used a public dataset made available on a
popular platform such as Kaggle, or made available through another type of
data repository.

  * •

Multiple. The authors used various sources to construct a new dataset.

The datasets also consisted of a wide range of data points. We created the
following ranges of data points:

  * •

$<$ 1,000. The dataset consists of less than 1,000 data points.

  * •

1,000-2,000. The dataset consists of more than 1,000 data points but less than
2,000 data points.

  * •

2,000-5,000. The dataset consists of more than 2,000 data points but less than
5,000 data points.

  * •

5,000-10,000. The dataset consists of more than 5,000 data points but less
than 10,000 data points.

  * •

10,000-50,000. The dataset consists of more than 10,000 data points but less
than 50,000 data points.

  * •

50,000-100,000. The dataset consists of more than 50,000 data points but less
than 100,000 data points.

  * •

100,000-500,000. The dataset consists of more than 100,000 data points but
less than 500,000 data points.

  * •

500,000-1,000,000. The dataset consists of more than 500,000 data points but
less than 1,000,000 data points.

  * •

$>$ 1,000,000. The dataset consists of more than 1,000,000 data points.

Finally, we also recorded the availability of the dataset, i.e. whether or not
the authors made the data used in their study available:

  * •

Yes. The authors published or provided a link to the dataset they created and
used for their studies.

  * •

Yes (ext). The dataset was already public. Hence, the dataset is (already)
available.

  * •

Upon request. The authors added a statement in their study regarding the
availability of their dataset following a (sometimes reasonable) request.

  * •

Partially. A part of the dataset is available. It can happen when the authors
take advantage of a public dataset and augment it with their own features.

  * •

No. The authors did not mention in their paper - whether in the core of the
text, in a footnote or in a statement at the end of the article - that the
dataset was available. When no such indication was mentioned, we considered
that the data were not available.

Tables 5 to 7 show a summary of the distribution of the selected studies by
data.

Table 5: Distribution of Studies by Data Sources. Data Source |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Bitcoin |  0 |  0 |  1 |  0 |  0 |  1 |  1 |  1 |  0 |  1 |  1 |  6
Blockchain.com |  0 |  0 |  2 |  0 |  2 |  3 |  2 |  1 |  2 |  1 |  0 |  13
Ethereum |  0 |  0 |  0 |  0 |  0 |  0 |  2 |  2 |  0 |  0 |  0 |  4
Etherscan.io |  0 |  0 |  0 |  0 |  1 |  1 |  3 |  5 |  5 |  6 |  2 |  23
Walletexplorer.com |  0 |  0 |  0 |  0 |  0 |  1 |  2 |  1 |  1 |  0 |  0 |  5
XBlock.pro |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  0 |  1 |  0 |  2
Public Dataset |  0 |  0 |  1 |  1 |  0 |  1 |  3 |  5 |  9 |  7 |  0 |  27
Multiple |  1 |  0 |  0 |  0 |  1 |  5 |  5 |  9 |  12 |  11 |  3 |  47
Other |  0 |  0 |  1 |  0 |  0 |  3 |  2 |  4 |  3 |  6 |  1 |  20
Unclear |  0 |  0 |  0 |  1 |  2 |  0 |  1 |  1 |  4 |  3 |  0 |  12
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159
Table 6: Distribution of Studies by Number of Data Points. Data Points |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
$<$ 1,000 |  0 |  0 |  0 |  0 |  1 |  0 |  0 |  0 |  1 |  1 |  0 |  3
1,000-2,000 |  0 |  0 |  0 |  0 |  2 |  0 |  0 |  1 |  1 |  2 |  0 |  6
2,000-5,000 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  3 |  4 |  1 |  0 |  9
5,000-10,000 |  0 |  0 |  0 |  0 |  0 |  1 |  2 |  0 |  3 |  0 |  3 |  9
10,000-50,000 |  0 |  0 |  0 |  0 |  0 |  0 |  3 |  4 |  3 |  5 |  1 |  16
50,000-100,000 |  0 |  0 |  0 |  0 |  1 |  0 |  1 |  0 |  3 |  3 |  0 |  8
100,000-500,000 |  0 |  0 |  0 |  0 |  0 |  3 |  1 |  3 |  7 |  5 |  1 |  20
500,000-1,000,000 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  3 |  0 |  1 |  0 |  4
$>$ 1,000,000 |  1 |  0 |  4 |  2 |  0 |  6 |  7 |  11 |  9 |  9 |  1 |  50
Unclear |  0 |  0 |  1 |  0 |  2 |  5 |  6 |  5 |  5 |  9 |  1 |  34
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159
Table 7: Distribution of Studies by Data Availability. Data Availability |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Yes |  0 |  0 |  0 |  0 |  1 |  5 |  3 |  2 |  7 |  8 |  3 |  29
Yes (ext) |  0 |  0 |  1 |  1 |  0 |  1 |  3 |  8 |  12 |  10 |  0 |  36
Upon request |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  2 |  3 |  6
Partially |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  0 |  1 |  0 |  1
No |  1 |  0 |  4 |  1 |  5 |  9 |  15 |  20 |  16 |  15 |  1 |  87
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159

###  5.4 Machine Learning and Distribution

The studies applied different types of analysis to the blockchain data:

  * •

Classification. The paper addressed the application of a supervised learning
approach and attempted to predict a label.

  * •

Clustering. The paper addressed the application of an unsupervised learning
approach and attempted to group data points together.

  * •

Deep Learning. The paper addressed the application of a neural network with
multiple layers.

  * •

Regression. The paper addressed the application of a supervised learning
approach and attempted to predict a continuous value.

  * •

Time Series Analysis. The paper aimed to analyze a time series.

  * •

Combination. The paper addressed the application of at least two algorithms
belonging to the aforementioned categories.

Table 8 shows a summary of the distribution of the selected studies by machine
learning type.

Table 8: Distribution of Studies by Model. Model |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  Total
---|---|---|---|---|---|---|---|---|---|---|---|---
Classification |  0 |  0 |  1 |  0 |  3 |  7 |  11 |  14 |  24 |  13 |  1 |  74
Clustering |  1 |  0 |  1 |  1 |  2 |  2 |  1 |  2 |  1 |  2 |  1 |  14
Deep Learning |  0 |  0 |  0 |  0 |  0 |  0 |  2 |  5 |  3 |  2 |  0 |  12
Regression |  0 |  0 |  0 |  0 |  0 |  3 |  0 |  1 |  1 |  3 |  0 |  8
Time Series Analysis |  0 |  0 |  3 |  0 |  1 |  0 |  2 |  0 |  0 |  1 |  0 |  7
Combination |  0 |  0 |  0 |  1 |  0 |  3 |  3 |  8 |  7 |  14 |  4 |  40
Other |  0 |  0 |  0 |  0 |  0 |  0 |  2 |  0 |  0 |  1 |  1 |  4
Total |  1 |  0 |  5 |  2 |  6 |  15 |  21 |  30 |  36 |  36 |  7 |  159

##  6 Mapping Results

As mentioned before, the goals of this study are to provide an overview of the
state of the art and to identify potential research gaps. We used heatmaps
here that allow us to quickly grasp the dimensions explored by current
research. The mapping results are represented in Figures 2 to 10.

Figure 2: Distribution papers by Use Case and by Blockchain

The first dimension of our classification scheme, namely the Use Case, is
discussed in more detail in Sections 6.1 to 6.6. The studies will be discussed
by use case and we will address the other dimensions of our classification
scheme as well, specifically the blockchain, the data, and the machine
learning model. We can already state that, as shown in Figure 3, the majority
of the studies we considered here addressed the problems of “Anomaly
Detection” (49.7%), “Cryptocurrency Price Prediction” (18.9%), and “Address
Classification” (17.6%). A smaller number of studies focused on “Smart
Contract Vulnerability Detection” (7.5%) or “Performance Prediction” (4.4%).
Three studies (1.9%) were classified as “Other”. [49] proposed a method for
behavior pattern clustering for blockchain nodes; [50] designed a solution to
automatically label unknown contracts on Ethereum; and finally [51] conducted
some data analysis on EOS blockchain data.

Figure 3: Distribution of papers by Use Case

As far as the blockchains are concerned, we can see from Figure 4 that the
majority of studies focused on a single particular blockchain, either Bitcoin
(47.1%) or Ethereum (42.8%). Some authors worked on both (3.8%) or on other
multiple blockchains (1.9%), e.g. Bitcoin, Ethereum and Ripple as in [52] or
EOSIO and Ethereum [53]. Finally, some studies analyzed the EOSIO blockchain
[51], Hyperledger Fabric [54], Steem [55]; or it was unclear which
blockchain(s) was (were) analyzed.

Figure 4: Distribution of papers by Blockchain

Next, Figure 5 shows that the majority of the studies analyzed here used
multiple data sources (29.6%), such as Blockchain.com, Etherscan.io, a public
dataset, and/or bitcoin clients. Other common sources include a single
blockchain explorer website (27.0%) such as Blockchain.com, Etherscan.io,
Walletexplorer.com, or XBlock.pro; or a public dataset (17.0%).

Figure 5: Blockchain Data Sources

We can also see from Figure 6 that most studies had a dataset consisting of
more than 1,000,000 data points (31.4%), between 100,000 and 500,000 data
points (12.6%), or between 10,000 and 50,000 data points (10.1%). We should
also note that a part of the studies we analyzed here (21.4%) were unclear
regarding the size of the dataset.

Figure 6: Blockchain Data Points Analyzed by Different Studies

Furthermore, Figure 7 displays the research data availability. Most studies
did not share the data they used for the research (54.7%), some were made
available by the authors (18.2%), and some were available by default because
of their public characteristic (22.6%). A small number of studies stated that
the data were available on request (3.8%), while the data for one study (0.6%)
were partially available.

Figure 7: Distribution papers by Data Available

Additionally, Figure 8 shows that the period covered by the datasets fall
mostly between 2013 and 2019.

Figure 8: Distribution of papers by Period Covered

Finally, when we look at the models applied to the data, we can see in Figure
9 that a large majority of the studies conducted a classification (46.5%). The
second most popular processing was a combination of models (25.2%), such as
classification and clustering, classification and deep learning, or
classification and regression for instance. Other frequent models include
clustering alone (8.8%) and regression alone (5.0%). Some authors used time
series analysis (4.4%) or deep learning (7.5%).

Figure 9: Distribution of papers by Type of Machine Learning Task

As far as the specific algorithms are concerned, we can state from Figure 10
that the Random Forest and the Support Vector Machine were the most popular
ones in the studies we cover here, followed by the Logistic Regression,
XGBoost, and K-Nearest Neighbors.

Figure 10: Distribution papers by Machine Learning Methods

In the next subsections, we discuss each use case in more detail. For each use
case, we provide a summary table reporting: the blockchain studied, the
availability of the data and code (Data-Code), and the models and the
performance measures used by the authors. We provide the best performance
reported by the authors (in parenthesis) and the algorithms used to achieve
that performance (in bold). Note that when no performance appears in the
table, it means that either it was not explicitly mentioned, or that it was
graphically represented in the paper. Also, when multiple ML models are
highlighted, it means that some performed best on some metrics, while other
algorithms performed best on some other measure. Finally, when experiments
were carried out on various datasets, or when the performance measures were
reported for the train and test sets; we decided to report only the best
measure. When experiments were carried out on various blockchains, we provided
the metrics for both platforms.

###  6.1 Address Classification - De-Anonymization

We found twenty-eight papers studying address classification.

In our study, fourteen papers addressed de-anonymization or actors
identification. Out of this set, fourteen papers focused on Bitcoin. Multiple
authors applied a classification task using different algorithms: NB [56];
AdaBoost, Linear SVM, LR, Perceptron, RF [57]; Multivariate Wald-Wolfowitz
test [58]; AdaBoost, Bagging, DT, Extremely Randomized Trees, GB, NN, RF [59];
AdaBoost, Bagging, DT, Extra Trees, GB, KNN, RF [60]. Various sets of features
were fed to these algorithms: transaction-related features [56, 58, 59, 60];
transaction-related features and graph-related features [57]. The datasets
used in these studies consisted of more than 1,000,000 data points [56, 58,
59, 60], and covered different periods: from September 29, 2011 to April 22,
2015 [57]; and until April 7, 2013 [58].

Various authors addressed the problem of Bitcoin de-anonymization using a
clustering algorithm and transaction-related features [61, 62, 63] . In [64,
65], the authors improved the Louvain clustering algorithm in order to make
Bitcoin de-anonymization possible, using a significant amount of Bitcoin
transactions (almost 300 millions, and more than 120GB respectively) and two
sets of features: transaction-related features and tracing data features. The
other datasets consisted of more than 1,000,000 data points[61, 62], covering
a period of about two months (March 10, 2020 to May 09, 2020) for [61].

Finally, four papers applied both Classification and Clustering: CatBoost, DT,
XGboost [66]; KNN [67]; and RF and various clustering models (Contraction,
SharedUser, K-Clique) [68]. The datasets consisted of 23 features describing
output transaction links [66]; of address statistical features and address
transaction history features [67]; or on-chain data and off-chain data [68].
Their size varied, from more than 1,000,000 data points [68], to less than
1,000,000 data points [66], and less than 500,000 [67]; and covered large
periods of time: January 3, 2009 to January 25, 2021 [66]; and from January
2009 to September 2016 [67]. Also, [69] proposed an Adaptive Weighted
Attribute Propagation (AWAP) enhanced community detection model. For their
solution, the authors selected 16 transaction-related features, and used a
dataset consisting of less than 10,000 data points.

[53] focused on identity inference on EOSIO and Ethereum, using a dataset
consisting of less than 5,000 accounts. The authors applied GNN to subgraphs
centered on target accounts.

In our study, eleven papers focused on address classification. The majority
(seven papers) of them studied address classification on Bitcoin, using: ANN
and RF [70]; Adaboost, DT, and imECOC [71]; AdaBoost with DT, LightGBM, LR,
MLP, NN, RF, SVM, XGBoost [72]; RF [73]; AdaBoost, GB, RF [74]; ABC, BG, CART,
ET, GB, KNN, LDA, LR, MLP, NB, RFC, SGD, SVM [75]; and C4.5, ID3, KNN, PART
[76]. As far as the selected features are concerned, [70] used transaction-
related features, making the distinction between transmission and recipient
addresses; [71] used address-related features and network metrics; [72] used
three types of features, namely basic statistics, extra statistics, and
moments; [73, 75, 76] extracted transaction history features; [74] used four
sets of features, specifically entity features, address features, and two
graph-related features. The periods covered by the datasets in the studies
here: from January 1, 2016 [70]; from January 3rd, 2009 to June 30, 2018 [72];
from January 9, 2009 to February 9, 2017 [73]; from the Bitcoin genesis until
February 2019 [74]; and the period 2011-2018 [76]. These periods led to a
dataset size of: less than 1,000,000 data points [70]; of more than 1,000,000
data points [72, 74, 76, 71]; of less than 500,000 data points [73]; less than
100,000 data points [75]

One paper applied classification and deep learning to study the address
classification, clustering and coin mixing problems on Bitcoin. Specifically,
the authors in [77] trained the following algorithms: LINE, LSTM Transaction
Tree, RF using GCN Feature, SGD optimizer. The authors used sequence features
extracted from transaction trees; and used a dataset of more than 1,000,000
data , covering the following periods: (i) November 16, 2013 to May 10, 2014;
(ii) May 12, 2016 to May 17, 2016.

Only two papers studied address classification on Ethereum. [78] focused on
address clustering and de-anonymization, using a dataset of more than
1,000,000 data points, spanning from July 30, 2015 to April 4, 2020. The
authors applied Node Embeddings. In [79], the authors, using a dataset of more
than 1,000,000 data points, applied a classification task and a deep learning
task. They selected node features; and fed them to the following algorithms:
Adam Optimizer, Cluster-GCN, Filter and Augment Graph Neural Network, GCN,
GNN, GraphSAGE, H2GCN.

Finally, [80] proposed PeerClassifier, a solution for blockchain peers
classification, using the daily transaction amounts of peers extracted as a
sequence.

Other papers (two in our study) centered on agent characterization. [81]
studied entities characterization on Bitcoin. The authors collected more than
1,000,000 data points, using blocks created before March 24 2018. The dataset
was composed of five feature classes (address features, entity features,
temporal features, centrality features, and motif features), and was fed to
LightGBM and LR. [82] focused on characterizing key agents on Ethereum. The
authors collected data between May 2, 2016 to June 29, 2019, for a total of
less than 50,000 data points. They selected 28 features (categorized as volume
features, temporal features, and structural features) and fed them to multiple
classifiers (LightGBM, LR, MLP, RF, SVM).

Table 9: Papers Addressing Address Classification Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[66] |  Bitcoin |  N-N |  Classification: Decision Tree, CatBoost, XGBoost; and Clustering |  Clustering: aNMI (0.796), Completeness (0.6661), Homogeneity (1.0), Rand Index (0.532), V-score/NMI (0.796); Classification: ROC-AUC score (1.0)
[69] |  Bitcoin |  Y-N |  Classification: AWAP (Enhanced Community Detection Model), BAGC, CP, DT, LightGBM, LR, RF, XGBoost; and Clustering: AWAP (Enhanced Community Detection Model), Big-CLAM, CESNA, Circles, CNM, CODICIL, CP, DeepWalk |  Classification: Accuracy (0.95), F-Score (0.92), Precision (0.91); Clustering: F-Score (0.7583), Jaccard (0.5875), NMI (0.5683)
[82] |  Bitcoin |  Y-N |  Classification: LR, SVM, MLP, RF, LightGBM |  Accuracy (0.893), F1 (0.985), Precision (1.0), Recall (0.98), Macro F1 (0.865), Macro Precision (0.888), Macro Recall (0.862)
[53] |  EOSIO and Ethereum |  Y-N |  Classification and Deep Learning: I2BGNN (Graph Neural Network), KNN, RF, SVM |  F1 (0.9950), Precision (0.9917), Recall (0.9986)
[81] |  Bitcoin |  N-N |  Classification: LightGBM, LR |  Accuracy (0.92), F1 (0.91), Precision (0.92)
[56] |  Bitcoin |  Y-N |  Classification: NB |  Unclear
[61] |  Bitcoin |  N-N |  Clustering: “Multi-Input Addresses” heuristic |  Reliability
[70] |  Bitcoin |  N-N |  Classification: ANN, RF |  Accuracy (0.844)
[71] |  Bitcoin |  N-N |  Classification: Adaboost, DT-CART, DT-HDDT), imECOC |  Accuracy (0.9147), AUC (0.9051), F1-measure (0.9117), G-mean (0.8283)
[63] |  Bitcoin |  N-N |  Clustering |  Unclear
[57] |  Bitcoin |  N-N |  Classification: AdaBoost, Linear SVM, LR, Perceptron, RF |  F1 (0.9973), Precision (0.9971), Recall (0.9976)
[62] |  Bitcoin |  Y-N |  Clustering |  aNMI (0.67), F1 (0.86), NMI (0.89), Precision (0.98), Recall (0.91)
[67] |  Unclear |  N-N |  Classification: KNN; and Clustering |  Accuracy (0.911), F1 (0.787), False Accept Rate (0.051), Precision (0.813), Recall (0.772), Validation rate (0,869)
[80] |  Unclear |  N-N |  Classification: DT, KNN, PeerClassifier, SVM |  Accuracy (0.745)
[68] |  Bitcoin |  N-N |  Classification: RF; and Clustering: Baseline, Contraction, K-Clique, SharedUser |  Classification: Accuracy (0.738), F1 (0.701), Precision (0,769), Recall (0.654); Clustering: aMI (0.2394), Rand index adjusted for chance (0.03), V-measure (0.5229)
[64] |  Bitcoin |  N-N |  Clustering |  Accuracy (1.0), Comprehensiveness (1.0)
[65] |  Bitcoin |  N-N |  Clustering |  Accuracy (0.91), Recall (1.0)
[58] |  Bitcoin |  Y-N |  Classification: Multivariate Wald-Wolfowitz test |  Accuracy (0.563), Equal Error Rate (0.084)
[59] |  Bitcoin |  N-N |  Classification: Adaptive Boosting, Bagging (Bootstrap Aggregation), DT, Extremely RT, GB, k-NN, RF |  Accuracy (0.8042), F1 (0.7964), Precision (0.8055), Recall (0.8083), ROC Curve
[60] |  Bitcoin |  N-N |  Classification: AdaBoost, Bagging Classifier, DT, Extra Trees, GB, KNN, RF |  Accuracy (0.78), F1 (0.76), Precision (0.75), Recall (0.78), Support (451)
[78] |  Ethereum |  Y-Y |  Classification: Node Embeddings |  AUC, Average rank
[72] |  Bitcoin |  Y-N |  Classification: AdaBoost, LightGBM, LR, NN, Perceptron, RF, SVM, XGBoost |  Macro-F1 (0.86), Micro-F1 (0.91)
[73] |  Bitcoin |  Y-N |  Classification: RF |  Accuracy (0.72)
[74] |  Bitcoin |  N-N |  Classification: Adaboost, GB, RF |  Accuracy (0.9968), F1 (1.0), Matthews Correlation Coefficient (0.99), Precision (1.0), Recall (1.0), Standard Deviations
[77] |  Bitcoin |  Y-Partially |  Classification: Large-scale information network embedding , RF; and Deep Learning: LSTM Transaction Tree, RF using GCN Feature, SGD Optimizer |  Classification: Accuracy, F1(0.983), Precision (0.982), Recall (0.990)
[79] |  Ethereum |  N-N |  Classification and Deep Learning: Adam Optimizer, Cluster-GCN, Filter and Augment GNN, GCN, GraphSAGE, H2GCN |  Macro-F1 (0.866), Macro-Precision (0.876), Macro-Recall (0.871), Micro-F1 (0.888)
[75] |  Bitcoin |  N-N |  Classification: AB, BG, CART, ET, GB, KNN, LDA, LR, MLP, NB, RF, SGD, SVM |  Accuracy (0.8076), F1 (0.8056), Precision (0.8027), Recall (0.8271), Support (214)
[76] |  Bitcoin |  Y-N |  Classification: C4.5, ID3 algorithm, KNN, PARTial Decision Tree (PART) |  Accuracy, Kappa (Cohen’s Kappa)

###  6.2 Anomaly Detection

In our study, we analyzed seventy-nine papers addressing Anomaly Detection:
Ponzi Scheme Detection (10 papers), Phishing detection (12 papers), Ponzi
Scheme and Phishing detection (1 paper), Intrusion or Attack Detection (4
papers), Illicit account detection (23 papers), Misbehavior or fraud detection
(27 papers), and Bot detection (2 papers).

In our study, we found ten papers working specifically on Ponzi scheme
detection. All approaches focus on Ethereum, except for [83] who address the
problem on Bitcoin. The authors built and made publicly available a dataset
consisting of about 10,000 addresses and features of Bitcoin Ponzi schemes. As
far as the papers focusing on Ethereum are concerned, authors used key account
features and opcodes [84, 85, 86, 87], transaction features and code features
[88], only the opcodes [89, 90, 91], transaction features, features and
bytecode [87]. The authors exploited a dataset consisting of: 1250 non-Ponzi
scheme contracts and 131 Ponzi scheme contracts [84, 92], 200 Ponzi scheme
contracts and 3580 non-Ponzi scheme contracts [85, 89, 91], 3203 non-Ponzi
scheme contracts and 172 Ponzi scheme contracts [88], 168 Ponzi schemes and
2851 normal smart contracts (the XBlock dataset) [86], 386 Ponzi schemes and
3239 non-Ponzi schemes [90], 3614 Non-Ponzi contracts and 810 Ponzi contracts
[91] The classifiers applied were: XGBoost[84, 89, 85], RF [89, 91, 88], SGD
and J48 [88], DT [89, 85, 91], Extremely Randomized Trees [89, 91], GB [89,
91], LightGBM [89, 87], Logistic Regression [89], SVM [89, 85], Behavior
Forest [92], Oversampling-based LSTM [86], Ordered Boosting [90], AdaBoost,
combination of Bagging-Tree and XGBoost, KNN [91], Isolation Forest [85].

Multiple authors addressed phishing detection on Ethereum, using
classification and/or deep learning. They used different types of models: CT-
GCN, Line graph and GCN, LR, One-Class SVM, SV, and XGBoost [93]; LightGBM,
DeepWalk, Node2Vec, LINE, GCN [94]; SVM [95]; Graph2Vec, Line Graph2Vec,
Node2Vec, and WL-kernel [96]; IF, LR, NV, One-Class SVM [97]; Graph2Vec,
Node2Vec, Sub2Vec [98]; AdaBoost, KNN, SVM [99]; Adam optimizer, BPNN, DT,
LightGBM, LR; LSTM, LSTM-FCN, Node2Vec, RF, RNN, SVM, Trans2Vec; [100];
GATNE-I (network embeddings), GNN, Random Walk[101]; DeepWalk, EGAT, GAT, GCN,
Graph2Vec, GraphSageNode2Vec, MLP, Softmax (comparison with previous works
where DElightGBM, LightGBM, MP-GCN, RF were used), Sub2Vec [102]; Adam
optimizer, GCN, GIN, Graph2Vec, GraphSAGE, SF [103]; 48 Consolidated, C4.5 DT,
Eth-PSD, Fast Decision Tree, JRip, NB Tree, OneR, PART Decision List [104].
The inputs for these models were graph-based (transactional) features [93, 94,
96, 98, 101, 102, 103, 97], account features and network features [99],
transaction features, state features, and transfer features [100], and
transaction-based features [104, 95]. The datasets were composed of reconized
phishing accounts and recognized non-phishing accounts; and were balanced [93,
96, 102] or unbalanced [94, 98, 99, 100, 104].

[105] addressed both Ponzi schemes and phishing scams detection on Ethereum
using data mining techniques. To address the first type of misbehavior, the
authors extracted seven account features and used the opcodes of the smart
contracts. They fed these features to an XGBoost algorithm and used a dataset
consisting of 10,000 transactions. As far as the phishing scam detection
problem is concerned, the authors used 7,795,044 transactions and compared
multiple classifiers (DT, Dual-sampling Ensemble SVM, Dual-sampling Ensemble
DT, Dual-sampling Ensemble lightGBM, LightGBM, and SVM).

Multiple papers in our study addressed fraud detection on Bitcoin [106, 107,
108, 109, 110, 111], on Ethereum [112], and on Bitcoin and Ethereum [113, 114,
115]. Various algorithms were used: an improved apriori algorithm [106];
k-means and a trimmed k-means [107]; Boosted LR, kd-trees, maximum-likelihood
based LR, RF, Trimmed k-means [108]; RF and XGBoost [109], LR, RF, XGBoost,
and Angle-based Outlier Detection, Cluster-based Outlier Factor, Isolation
Forest, KNN, Local Outlier Factor, One-Class SVM, and PCA [110]; k-means
[111]; AB, BT, DT, Ensemble models, ET, GB, LGBM, RF, XGB [112]; RF, XGBoost
[113] ; Adam optimizer, GCN, MLP decoder [114]; and LR, RF, SVM [115]. The
Elliptic dataset [116] was used by [110] and used and augmented by [114]. The
sets of features selected or extracted by the authors include: transaction
features [106, 109, 110, 113]; currency features, network features, and
average neighborhood features [107, 108]; Turnover-features, connectivity-
features, activity-features, utxos-specific-features [111]; opcode n-grams,
transaction data, source code characters [112]. Except for [110, 113, 109],
the dataset consisted of more than 1,000,000 data points.

Next, 17 papers addressed abnormality detection on Bitcoin [117, 118], on
Ethereum [119, 120, 121, 122, 123, 124, 125, 126, 127], and on Bitcoin and
Ethereum [128, 129, 130, 131, 132, 133]. The authors addressed the problem
using classification, deep learning and/or clustering: ANN and RF [117]; GCN
[118]; Extra Trees, GB, LR, MLP, RF, and XGBoost [128]; AdaBoost, CatBoost,
DT, KNN, LightGBM, LR, MLP, RF, SVC, and XGBoost [129]; AdaBoost, KNN, LOF,
Mahalanobis distance-based method, MLP, OCSVM, RF, and SVM [130]; KNN, NB, and
SVM [131]; BTCOut, CTOuliers, DBSCAN, k-medoids algorithm, OddBall, Tclust
[132]; Apriori, Self Organised Maps [119]; k-means and RF [127]; GCN, GNN,
IForest, OC-GAT, OC-GCN, OC-SAGE, OCSVM [120, 121]; DT, Kernel SVM, KNN, LR,
NB, RF, and One-class SVM [122]; CNN, GAT, GCN, Heterogeneous Graph
Transformer Networks, RCNN, SVM [123]; sequence to sequence recurrent
autoencoder [124]; LSTM [125]; DT, Isolation Forest and RF [126]; and K-Means
[134]. The Elliptic dataset [116] was used by [118, 128, 129] and was
augmented for the authors studying Ethereum in addition to Bitcoin. The
Ethereum Classic dataset [135] (composed of 2179 fraudulent accounts and 2502
normal accounts) was used in [122, 124]. The size of the datasets used in
these studies varied from more than 1,000,000 data points [117, 130, 132, 133,
119, 121, 124], and less than 500,000 data points [131, 127, 120, 122, 123,
125, 126]. The features used as input for the models include: transaction-
based features [117, 131, 122, 125, 126]; graph-based transaction features or
transactions depicted as graph [130, 132, 133, 120, 121]; addresses (Smart
Contract addresses and user addresses) and transactions [119]; features
extracted from wallet transaction and wallets data [127]; account and code
features [123]; blocks features and transaction features [124]; and time
series transaction data [134].

In our study, 23 papers addressed the illicit account or illicit node
detection problem on Bitcoin [136, 137, 138, 139, 140, 141, 142, 143, 144,
145] and on Ethereum [146, 135, 147, 148, 116, 149, 150, 151, 152, 153, 154,
155, 156]. For this class of problem, various algorithms were used: DT, IF,
IS, LR, OCSVM, PU learning [136]; ANN, LSTM, RF, SVM with RBF kernel, and
XBGoost [137]; GB and RF [138]; LR, RF, and SVM [139]; Evolve-GCN, GCN, GCN
and MLP, LR, MLP, RF, Skip-GCN, Temporal GCN [140]; GCN, Graph Enhanced RF
with Feedback model, Graphlet Spectral Correlation Analysis, RF; and
Regression [141]; AdaBoost, BN, k-means, NB, and RF [142]; CatBoost, LGBA, RF,
XGBoost [143]; GCN, Node Embeddings, MLP [144]; Attention Mechanism-based GCN,
GCN, MLP, MP-GAT, Node Embeddings, Skip-GCN [145]; RF, SVM, and XGBoost [146];
XGBoost [135]; Birch algorithm [147]; ASXGB, CatBoost, LGB, RF, XGBoost [148];
DT, GB, KNN, MLP, RF, SVM [149]; ETH Tracking Tree Method, LightGBM, LSTM; RF,
XGBoost [150]; Ensemble methods, LR, RF, and SVM [151]; DT (J48), KNN, and RF
[152]; AdaBoost, KNN, LGBM, LR, MLP, RF, SVM, and XGBoost [153]; C5, CatBoost,
CAT Tree, LGB, NN, SVM; and Clustering [154]; LR, SVM, and XGBoost [155].
Multiple authors used the Elliptic dataset to train and test their models
[139, 140, 141, 142, 143, 144, 145, 148] or the Ethereum Classic dataset [143,
153, 154], or another public dataset [157] consisting of 10 millions of
transactions [147]; otherwise, the authors collected their own data: [136]
collected 3 snapshots of 1,500,000 Bitcoin transactions; [137] used 24,720
illicit addresses and 1,209,850 licit addresses; [138] exploited 21 Darknet
Market-related and 351 non-Darknet Market-related Bitcoin entities with the
corresponding addresses and transactions; [146] collected 2,200 wallets
documented as involved in illegal activity and transactions from 349,999
randomly selected non-fraudulent wallets; [149] collected blocks between
9,000,000 and 10,999,999, and 16,108,509 addresses and 141,242,377
transactions; [150] used 5585 labeled addresses; [151] took advantage of
18,000,000 transactions; [152] used 7,809 transactions (7,651 normal and 159
fraudulent transactions); and [155] used 2,600 illicit labeled addresses and
20,000 random addresses. As far as the features are concerned, the authors
extracted and used: topological and temporal features [137]; transaction-
based, address-based, block-based and statistics [138]; local features, local
and aggregated features, local and node embeddings, and aggregated and node
embeddings [139, 148]; local features [140]; local and aggregated features
[144]; normal transaction-based features and erc20 token transaction-based
features [135]; Ether trading transactions, smart contract creation and smart
contract invocation and account-based features (external owned account and
smart contract account) [147]; local and global features [149]; transaction-
based features and sequence features (information of the ETH flow) [150];
general features, neighborhood features, local features, timestamp-related
features [151]; transaction-based features [152, 146]; account data,
transactional data, and history data [155]. Finally, [156] proposed SigTran, a
graph-based method for the detection of illicit nodes on Ethereum and Bitcoin.
The authors used the dataset made public by [116] and extracted four sets of
features: structural features, transactional features, regional features, and
neighborhood features. They reported a better performance for their tool,
compared to existing works. Specifically, the results for the Bitcoin and
Ethereum blockchains respectively were: precision scores were 0.905 and 0.944,
the recall scores were 0.947 and 0.940, the F1 scores were 0.918 and 0.942,
the accuracy scores were 0.915 and 0.942, and the AUC scores were 0.976 and
0.976.

Four papers in our study focus on attack or intrusion detection on Ethereum
[158, 159] and on Bitcoin, Bytecoin and Monero [160], using Classification
and/or Deep Learning: LSTM and RNN [158]; CNN, DT, KNN, and MLP [159], RF
[160]; and DT, Ensemble methods, KNN, MLP, SGD, and SVM [161]. The datasets in
these studies consisted of block-related features, gas-related features, and
transaction-based features (Ethereum Classic dataset) [158]; of transaction-
based features, block-based features, blockchain-based features [159]; and of
traffic-related features [160].

Finally, a couple of papers worked on bot detection. [162] proposed a solution
for bot detection on the Bitcoin blockchain, using a One-class SVM . With a
dataset consisting of more than 100,000 data points, and consisting of about
15 transaction-related features. [55], on the other hand, worked on the same
problem but focused on the Steem blockchain, and more specifically on posting
bot detection on the Steem blockchain. The authors collected data about 984
accounts, divided into 325 bot accounts and 659 human accounts. They extracted
various post-related features using the Minimum Average Cluster from
Clustering Distance between Frequent words and Articles. Finally, they tested
various classifiers (AdaBoost, DT, LightGBM, LSV, MLP, RF with Entropy, RF
with Gini, XGBoost).

Table 10: Papers Addressing Anomaly Detection Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[158] |  Ethereum |  N-N |  Deep Learning: LSTM, RNN |  Confusion matrix
[131] |  Bitcoin |  N-N |  Classification: KNN, NB, SVM |  Energy value, Accuracy (0.98)
[123] |  Ethereum |  Y-N |  Classification and Deep Learning: CNN, GAT, GCN, Heterogeneous Graph Transformer Networks, RCNN, SVM |  Macro-F1 (0.82), Micro-F1 (0.83)
[86] |  Ethereum |  Y-N |  Classification: Recurrent Neural Network - LSTM |  F (0.96), Precision (0.97), Recall (0.96)
[128] |  Bitcoin and Ethereum |  Y-N |  Classification: Extra Trees, GB, LR, MLP, RF, XGBoost |  Accuracy (0.9802 - 0.9891), AUC (0.951 - 1.0), F1 (0.8239 - 0.9760)
[160] |  Bitcoin, Bytecoin, Monero |  N-N |  Classification: RF |  AUC (0.9908), F1 (0.96), FPR, TPR
[90] |  Ethereum |  N-N |  Classification: Ordered Boosting |  F (0.96), Precision (0.95), Recall (0.96)
[135] |  Ethereum |  N-N |  Classification: XGBoost |  Accuracy (0.963), AUC (0.994)
[91] |  Ethereum |  N-N |  Classification: AdaBoost, combination of Bagging-Tree and XGBoost, DT, Extra trees, GB, KNN, RF |  Accuracy, AUC, F1 (0.95), FNR, FPR, Precision (0.97), Sensitivity (0.93), TNR, TPR
[161] |  Unclear |  N-N |  Classification: Ensemble methods, DT, KNN, MLP, SGD, SVM |  Accuracy, Precision, Recall
[105] |  Ethereum |  Y-N |  Classification: DT, Dual-sampling Ensemble DT, Dual-sampling Ensemble, Dual-sampling Ensemble lightGBM, LightGBM, SVM, XGBoost |  AUC (- - 0.8097), F (0.86 - -), F1 (- - 0.8122), Precision (0.94 - 0.8196), Recall (0.81 - 0.8050)
[134] |  Agnostic |  N-N |  Clustering: K-Means |  Accuracy (0.982)
[93] |  Ethereum |  Y-Y |  Classification and Deep Learning: CT-GCN, Line graph and GCN, LR, One-Class SVM, SV, and XGBoost |  Accuracy (0.8808), F1 (0.8814)
[118] |  Bitcoin |  Y-N |  Deep Learning: ARMAConv, ClusterGCNConv, GAT, GCN, GNN-FiLM, GraphConv, GraphSAGE, LEConv, Linear Regression, LR, MFConv, MLP, SGConv, SuperGATConv, TAGCN, TransformerConv |  F1 (0.7298), Precision (0.9527), Recall (0.6334)
[146] |  Ethereum |  N-N |  Classification: RF, SVM, XGBoost |  F1 (0.4470), FN, FP, FPR (0.002), Precision (0.8571), Recall (0.8747), Specificity (0.9998), TN, TP
[121] |  Ethereum |  Y-N |  Deep Learning: IForest, OC-GAT, OC-GCN, OC-SAGE, OCSVM |  Accuracy (0.9075), AUROC, F1 (0.8346)
[156] |  Bitcoin and Ethereum |  Y-Y |  Classification: Node2Vec, RiWalk, SIGTRAN (LR) |  Accuracy (0.915 - 0.942), AUC (0.976 - 0.976), F1 (0.918 - 0.942), Precision (0.905 - 0.944), Recall (0.947 - 0.940)
[124] |  Ethereum |  Y-N |  Deep Learning: Sequence to sequence recurrent autoencoder |  Unclear
[84] |  Ethereum |  Y-N |  Classification: XGBoost |  Precision (0.94), Recall (0.81), F-score (0.86)
[106] |  Bitcoin |  Y-N |  Frequent Set Mining: A Priori |  -
[94] |  Ethereum |  N-N |  Classification and Deep Learning: DeepWalk, GCN, LightGBM, LINE, Node2Vec |  AUC (0.5866), F1 (0.2636)), Precision (7294), Recall (0.1735))
[125] |  Ethereum |  N-N |  Deep Learning: LSTM |  F1 (0.77), Precision (0.88), Recall (0.70)
[137] |  Bitcoin |  N-N |  Classification and Deep Learning: ANN, LSTM, RF, SVM with RBF kernel, XGBoost |  F1 (0.9069), Precision (0.9567), Recall (0.8805)
[89] |  Ethereum |  Y-N |  Classification: DT, Extremely Randomized Trees, GB, LightGBM, LR, RF, SVM, XGBoost |  F1 (0.95), Precision (0.98), Recall (of 0.93)
[126] |  Ethereum |  N-N |  Outlier Detection: DT, Isolation Forest, RF |  -
[147] |  Ethereum |  Y-N |  Clustering: Birch algorithm |  -
[136] |  Bitcoin |  N-N |  Classification: DT, IF, IS, LR, OCSVM, PU learning |  FPR (0.0334), G-Mean (0.9479), TPR (0.9388)
[96] |  Ethereum |  N-N |  Classification: Node2Vec, Graph2Vec, Line Graph2Vec, WL-kernel |  F1 (0.73), Precision (0.69), Recall (0.77)
[162] |  Bitcoin |  N-N |  Classification: One-class SVM |  AUC (0.99), FPR (0.01), TPR (1.0)
[132] |  Bitcoin |  Y-N |  Classification: BTCOut, CTOuliers, DBSCAN, k-medoids algorithm, OddBall, Tclust |  F2 (0.473), Precision (0.433), Recall (0.602)
[133] |  Bitcoin |  N-N |  Clustering: Graph-based method |  -
[117] |  Bitcoin |  N-N |  Classification: ANN, RF |  F1 (0.9868)
[138] |  Bitcoin |  N-N |  Classification: GB, RF |  F1, Precision, Recall
[107] |  Bitcoin |  Y-N |  Clustering: k-means, Trimmed k-means |  Number known anomalies that were detected successfully
[119] |  Ethereum |  Partially-Y |  Clustering: Apriori, Self Organised Maps |  -
[151] |  Ethereum |  N-N |  Classification: AdaBoost, LR, RF, Stacking, SVM |  Accuracy (0.998), F1 (0.998), Precision (0.997), Recall (1.0)
[152] |  Ethereum |  Y-N |  Classification: DT (j48), KNN, RF |  Accuracy (0.9877), F (0.987), Precision (0.986), Recall (0.988)
[113] |  Bitcoin and Ethereum |  Y-N |  Classification: RF, XGboost |  AUC (0.92), Precision, Recall
[95] |  Ethereum |  N-N |  Classification: SVM |  F (0.846), Precision (0.871), Recall (0.822)
[97] |  Ethereum |  N-N |  Classification: IF, LR, NV, One-Class SVM |  F (0.908), Precision (0.927), Recall (0.893)
[85] |  Ethereum |  Y-N |  Classification: DT, IF, One-class SVM, SVM, XGBoost |  F (0.79), Precision (0.95), Recall (0.69)
[88] |  Ethereum |  Y-N |  Classification: J48, RF, SGD |  F (0.97), Precision (0.99), Recall (0.97)
[87] |  Ethereum |  Y-Partially |  Classification: Improved LightGBM |  Accuracy, AUC (0.992), F (0.967), Precision (0.967), Recall (0.967)
[92] |  Ethereum |  Y-N |  Classification: Behavior Forest |  F, Precision (0.956), Recall (0.93)
[143] |  Bitcoin |  Y-N |  Classification: CatBoost, LGBA, RF, XGBoost |  Accuracy (0.98), F1 (0.98), Recollect (0.976), Sensitivity (0.988)
[83] |  Bitcoin |  Y-Y |  Classification: Bayes Network, RF, RIPPER |  Accuracy (0.988), AUC (0.978), F (0.443), G-mean (0.978), Precision (0.287), Recall (0.969), Specificity (0.987)
[127] |  Ethereum |  N-N |  Classification: RF; and Clustering: k-Means |  F1 (0.96), Precision (0.96), Recall (0.96), Support (9632)
[109] |  Bitcoin |  N-N |  Classification: RF, XGboost |  F1, FPR, TPR
[108] |  Bitcoin |  N-N |  Classification: Boosted LR, kd-trees, Maximum-likelihood based LR, RF; and Clustering: Trimmed k-means |  Kappa (0.99), Precision (0.99), Recall (1.0), ROC (0.99), Sensitivity
[144] |  Bitcoin |  Y-N |  Classification and Deep Learning: GCN, MLP, Node Embeddings |  Accuracy (0.974), F1 (0.773), Precision (0.899), Recall (0.678)
[110] |  Bitcoin |  Y-Partially |  Classification: LR, RF, XGBoost; and Anomaly Detection: Angle-based Outlier Detection, Cluster-based Outlier Factor, Isolation Forest, KNN, Local Outlier Factor, One-Class SVM, PCA |  F1 (0.83)
[153] |  Ethereum |  Y-N |  Classification: AdaBoost, KNN, LGB, LR, MLP, RF, SVM, XBoost; and Clustering |  Accuracy (0.9860), F1 (0.9486), Precision (0.9948)
[154] |  Ethereum |  Y-N |  Classification: C5, CatBoost, CAT Tree, LGB, NN, SVM; and Clustering |  Accuracy (0.947), AUC (0.9846)
[145] |  Bitcoin |  Y-N |  Classification and Deep Learning: Attention Mechanism-based GCN, GCN, MLP, MP-GAT, Node Embeddings, Skip-GCN |  Accuracy (0.973), F1 (0.767), Precision (0.868), Recall (0.688)
[155] |  Ethereum |  N-N |  Classification: LR, SVM, XGBoost |  Accuracy (0.99), F1 (0.94), Precision (0.90), Recall (1.0)
[120] |  Ethereum |  N-N |  Classification and Deep Learning: EVANGCN, GCN, OCGCN |  F1 (0.8702), MicroAvg F1 (0.7788), Precision (0.7284), Recall (0.6409)
[114] |  Ethereum |  Y-Y |  Classification and Deep Learning: Adam optimizer, GCN, MLP |  Accuracy (0.9813), F1 (0.7735), Precision (0.8225), Recall (0.8387)
[148] |  Bitcoin and Ethereum |  Y-N |  Classification: ASXGB, CatBoost, LGB, RF, XGBoost |  Accuracy (0.981 - 0.989), F1 (0.940 - 0.983), Precision (0.988 - 0.985), Recall (0.931 - 0.981)
[112] |  Ethereum |  Y-N |  Classification: AB, BT, DT, Ensemble models, ETC, GB, LGBM, RF, XGB |  Accuracy (0.8967), F1 (0.8874), FNR (0.0018), FPR (0.1785), Precision (0.9837), Sensitivity (0.8148), TNR (0.9981), TPR (0.8214)
[98] |  Ethereum |  N-N |  Classification: Ego-Graph Embeddings, Graph2Vec, Node2Vec, Sub2Vec |  F1 (0.8199), Precision (0.8132), Recall (0.8271)
[139] |  Bitcoin |  Y-N |  Classification: LR, RF, SVM |  Accuracy (0.98851), Precision (0.65901), Recall (0.44866)
[99] |  Ethereum |  N-N |  Classification: AdaBoost, KNN, SVM |  Attack Success Rate, AUC (0.9276), Average number of Modified Edges, F1 (0.94), Precision (0.96), Recall (1.00)
[100] |  Ethereum |  N-N |  Classification: DT, LR, RF, SVM; and Deep Learning: Adam optimizer, LightGBM LSTM, LBPS (STM-FCN and BP neural network), Node2vec, RNN, Trans2vec |  Accuracy (0.9730), F1 (0.9786), Precision (0.9813), Recall (0.9759)
[140] |  Bitcoin |  Y-N |  Classification and Deep Learning: Evolve-GCN, GCN, GCN and MLP, LR, MLP, RF, Skip-GCN, Temporal GCN |  Accuracy (0.977), F1 (0.806), Precision (0.927), Recall (0.713)
[101] |  Ethereum |  N-N |  Classification: DT, GATNE-I (Network Embeddings), GNN, LR, NB, One-Class SVM, Random Walk |  F1 (0.957), PR-AUC (0.889), ROC-AUC (0.959)
[122] |  Ethereum |  Y-N |  Classification: DT, Kernel SVM, KNN, LR, NB, One-class SVM, RF |  Accuracy (0.99), Detection rate (0.953), FPR (0.0005)
[141] |  Bitcoin |  Y-N |  Classification and Deep Learning: GCN, Graph Enhanced RF with Feedback model, Graphlet Spectral Correlation Analysis, RF; and Regression |  F1 (0.821)
[111] |  Bitcoin |  Y-N |  Clustering: k-Means |  CoinJoin-related metrics
[149] |  Ethereum |  Y-N |  Classification: DT, GB, KNN, MLP, RF |  Accuracy (0.985), F1 (0.743), Precision (0.682), Recall (0.876)
[150] |  Ethereum |  Y-Y |  Classification and Deep Learning: ETH Tracking Tree Method, LightGBM, LSTM, RF, XGBoost |  Accuracy (0.95), F1 (0.9542), Precision (0.961), Recall (0.9469)
[102] |  Ethereum |  Y-N |  Classification: DeepWalk, EGAT, GAT, GCN, Graph2vec, GraphSage, MLP, Node2vec, Softmax, Sub2vec |  Accuracy (0.981), F1 (0.979), Precision (0.966), Recall (0.993)
[103] |  Ethereum |  Y-N |  Classification and Graph Learning: Adam Optimizer, GCN, Graph Isomorphism Network, Graph2Vec, GraphSAGE, SF |  Accuracy (0.9878), AUROC (0.9876), F1 (0.9878), Precision (0.9878), Recall (0.9880)
[159] |  Ethereum |  Y-N |  Classification: DT, KNN, MLP; and Deep Learning: CNN |  Accuracy (0.8754), F1 (0.85), Precision (0.8768), Recall (0.8448)
[115] |  Bitcoin and Ethereum |  N-N |  Classification: LR, RF, SVM |  Accuracy (0.987-0.834), F1 (0.994-0.909), Recall (0.897-0.835)
[104] |  Ethereum |  N-N |  Classification: 48 Consolidated, C4.5 DT, Eth-PSD, Fast DT, JRip, NB Tree, OneR, PART decision List |  Accuracy (0.9776), F1 (0.97), FPR (0.01), Precision (0.97), Recall (0.97), ROC (0.97), Time taken to build a model (0.03)
[55] |  Steem |  N-N |  Classification: AdaBoost, DT, LightGBM, Linear Support Vector, MLP, RF with Entropy, RF with Gini, XGBoost |  Accuracy (0.9268), F1 (0.8832), Precision (0.8512), Recall (0.9250)
[142] |  Bitcoin |  Y-N |  Classification: AdaBoost, Bayes Network, NB, RF; and Clustering: k-Means |  FPR (0.007), Number of Correctly Classified Instances (0.9948), Number of Incorrectly Classified Instances (0.0052), PRC (1.0), Precision (0.995), Recall (0.995), ROC (1.0), TPR (0.995)
[129] |  Bitcoin |  Y-N |  Classification: AdaBoost, CatBoost, DT, KNN, LightGBM, LGBM, LR, MLP, RF, SVC, XGB, XGBoost |  Accuracy (0.9921), F1 (0.957), Index of Balanced Accuracy (0.9599), Precision (0.997), Recall (0.922)
[130] |  Bitcoin |  Y-N |  Classification: AdaBoost, KNN, LOF, Mahalanobis Distance-Based Method, MLP, OCSVM, RF, SVM |  Accuracy (0.995), F1 (0.959), Precision (0.981), Recall (0.97)

###  6.3 Cryptocurrency Price Prediction

We found thirty papers addressing cryptocurrency price prediction.

The great majority of these studies focused on Bitcoin. Several authors
applied a regression and/or deep learning, using different sets of algorithms:
(i) ANN, LSTM, and RF [163]; Multiplayer Dynamic Game Model and SVM [164];
Bayesian NN, LR, and SVR [165]; Graph Chainlets and RF [166]; Bayesian
Regression and a GLM/Random forest [167]; and a GRU, GRU-Dropout, GRU-Dropout-
GRU, LSTM, and NN [168].

The authors included the following features into their models: technology and
economic factors [163, 165, 167, 168], game theory-related factors [164]; and
graph chainlets [166].

The datasets used in these papers covered different periods of time: three
different periods in [163] (specifically: (i) August 1, 2011 - December 31,
2013; (ii) August 1, 2013 - December 21, 2014; (iii) July 1, 2014 - December
31, 2017; (iv) July 1, 2015 - July 31, 2018); from September 13, 2011, to July
21, 2017 [165]; from 2008 to 2009 [166]; and from January 1, 2010 to June 30,
2019 [168]

Six papers applied a time series analysis to Bitcoin data, with different
periods of time: from October 2013 - March 2021 [169]; from January 2013–May
2017 [170]; from September 4, 2011 to February 28, 2014 [171]; from October
27, 2014 to January 12, 2015 [172]; until December 31, 2014 [173]. In [174],
the authors studied two periods: (i) January 1, 2011 to December 31, 2013;
(ii) July 1, 2013 to December 31, 2014. The features used in these papers
include both technology factors and economic factors.

Nine papers addressed the price prediction problem from a classification
and/or deep learning perspective, and tried to predict the direction of the
price movement instead of the price point. The algorithms used in these papers
are: Linear Discriminant Analysis, LR, LSTM, Quadratic Discriminant Analysis,
RF, SVM, XGBoost [175]; Ensemble, Feedforward NN, GB, GRU, LSTM, RNN, RF
[176]; a regression for classification task [177]; DT, KNN, Linear
Discriminant Analysis, LR, Quadratic Discriminant Analysis, RF, SVM, XGBoost
[178]; CNB, CNN, CNN-LSTM, CNN-GRU, DT, GRU, LR, LSTM, SVM, RF [179]; BPNN,
PCA-SVR, SDAE, and SVR [180]; CNN, Combinations of CNNs and RNNs, DNN, DRN,
Ensemble Models, RNN and LSTM [181]; DNDT, DRCNN, DSVR [182]; and CNN [183].
In order to train and test these models, the authors used various sets of
features: technology features, economic features and attention features [175,
176, 178, 179, 182]; technology features and economic features [180, 181,
183]; and Bitcoin daily transaction graphs [177]. Finally, the authors used
data covering various periods of time: from July 2013 - December 2019 [180];
from November 29, 2011 to December 31, 2018 [181]; from 2011 to 2019, with a
quarterly frequency of data [182]; and from 2015 [183]. In [176], the authors
used a dataset covering the period from March 4, 2019 to December 10, 2019.
Two papers used two datasets: [175] ((i) February 2 2017 to February 1 2019,
and (ii) July 17 2017 to January 17 2018); and [178] ((i) data and Bitcoin
daily price from January 1, 2017, to December 31, 2019; and (ii) Bitcoin 5-min
interval price for the period November 2, 2016 to June 11, 2018). The authors
in [179] used a dataset of less than 100,000 data points.

Four papers applied both a classification and a regression to address the
cryptocurrency price prediction problem. The algorithms used in the papers we
analyzed here include: ANN, Ensemble, RNN, and SVM [184]; ARIMA, LSTM, and RNN
[185]; ANN, LSTM, SANN, and SVM [186]; and Deep Cross Networks, DNN, and FLRDS
[187]. The datasets used in the studies consisted of various sets of features:
Bitcoin price-related features and transaction fees [184]; and technology
features and economic features [185, 186, 187]. The datasets used for these
studies covered various numbers of periods: two periods ((i) August 19, 2013 -
July 19, 2016; (ii) April 1st, 2013 - April 1st, 2017) in [184]; one period
(August 19, 2013 to July 19, 2016) in [185] and (from February 2014 -
September 2021) in [187]; and three periods ((i) April 1, 2013 to July 19,
2016; (ii) April 1, 2013 to April 1, 2017; (iii) April 1, 2013 to December 31,
2019) in [186].

Finally, for the Bitcoin price prediction, [188] used Multimodal Causality
Testing with technology features, economic features and attention features,
and data spanning from February 2018 - January 2020; while [189] used various
algorithms (i.e. Multi-Window Prediction Framework, Full connected NN, and
SVM). To get this result, the authors extracted transaction subgraphs and used
data from April 1, 2013 to December 31, 2017.

[190] analyzed Ether price prediction, with data ranging from August 11, 2015
to November 28, 2018. They predicted the cryptocurrency price using SVM; ANN
with technology features and economic features.

One paper studied price prediction on Bitcoin and Ethereum [191]. The authors
used technology features and economic features as input to several regressors
(ANN, Conjugate Gradient, Elastic Net, GB, LASSO, LR, LSTM, RF). They used a
dataset of less than 2,000 data points covering a period from December 18,
2017 - November 30, 2020.

Finally, [52] focused on cryptocurrency price prediction on Bitcoin, Ethereum,
and Ripple. The authors used a dataset consisting of more than 1,000,000 data
points and composed of the technology features, economic features and
attention features. They used various regressors (Conjugate Gradient Approach,
Linear Regression, LSTM, regression with GB, regression with RF).

Table 11: Papers Addressing Cryptocurrency Price Prediction Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[190] |  Ethereum |  N-N |  Regression: ANN, SVM |  MAPE (0.048), RMSE (0.068)
[188] |  Bitcoin |  Y-Y |  Multimodal Causality Testing: Multiple-Output Convolutional Gaussian |  Hypotheses Testing
[169] |  Bitcoin |  N-N |  Time Series Analysis: VAR |  Unclear
[175] |  Bitcoin |  N-N |  Classification and Deep Learning: Linear Discriminant Analysis, LR, LSTM, Quadratic Discriminant Analysis, RF, SVM, XGBoost |  Accuracy (0.672), F1 (0.776), Precision (0.817), Recall (0.840)
[163] |  Bitcoin |  N-N |  Regression and Deep Learning: ANN, LSTM, RF |  DA (76.5), MAE (8.7512), MAPE (2.2793), RMSE (12.0632)
[176] |  Bitcoin |  N-N |  Classification and Deep Learning: Ensemble, Feedforward NN, GB, GRU, LSTM, RF, RNN |  Accuracy (0.56)
[184] |  Bitcoin |  N-N |  Classification: ANN, Ensemble, SVM; and Regression: ANN, RNN, SVM, |  Accuracy (0.6291), AUC (0.58), MAE (6.7), MAPE (1.14%), RMSE (12.12)
[180] |  Bitcoin |  N-N |  Deep Learning: BPNN, PCA-SVR, SDAE, SVR |  DA (0.5985), MAPE (0.1019), RMSE (160.63)
[52] |  Bitcoin, Ethereum, and Ripple |  N-N |  Regression: ANN, Combination Forecasts, Elastic Net, GB, LAD, LASSO, Monitoring Forecasts, Rank Regression, RF, Shrinkage Estimators |  Accuracy (0.561-0.538-0.557), CSSED, MAD (2.68-3.53-3.18), R2 (0.0269-0.0171-0.0212)
[164] |  Bitcoin |  N-N |  Regression: Multiplayer Dynamic Game Model, SVM |  Accuracy (0.642), Precision (0.877), Sensitivity (0.439), Specificity (0.623)
[177] |  Bitcoin |  N-N |  Classification: Regression for classification task |  AUC PR (0.51), AUC ROC (0.73)
[165] |  Bitcoin |  N-N |  Regression: Bayesian NN, Linear Regression, SVR |  MAPE (0.0198-0.6302), RMSE (0.0244-0.5114)
[170] |  Bitcoin |  N-N |  Time Series Analysis: Bayesian Structural Time Series |  MAE (12.139), MSE (457.65), sMAPE (2.970%)
[191] |  Bitcoin and Ethereum |  N-N |  Regression: Conjugate Gradient, Linear Regression, LSTM, Regression with GB, Regression with RF |  MAE (- - -), RMSE (- - -)
[181] |  Bitcoin |  N-N |  Deep Learning: Combinations of CNNs and RNNs, CNN, DNN, Deep Residual Networks, Ensemble Models, Recurrent Neural Networks and LSTM, SVM |  Accuracy (0.5306), F1 (0.67), MAPE (3.60%), Precision (0.5290), Recall (1.0), Specificity (0.5370)
[171] |  Bitcoin |  N-N |  Time Series Analysis |  Unclear
[182] |  Bitcoin |  N-N |  Deep Learning: DNDT, DRCNN, DSVR |  Accuracy (0.9527), MAPE (0.29), RMSE (0.66)
[174] |  Bitcoin |  Y-N |  Time Series Analysis: Time series using ARDL Model |  F-Statistics, R2 (0.284)
[185] |  Bitcoin |  N-N |  Classification and Deep Learning and Regression: ARIMA, LSTM, RNN |  Accuracy (0.5278), Precision (1.0), RMSE (5.45%), Sensitivity (0.4040), Specificity (1.0)
[186] |  Bitcoin |  N-N |  Classification and Deep Learning and Regression: ANN, LSTM, SANN, SVM |  Accuracy (0.64), AUC (0.66), F1 (0.71), MAE (1.24), MAPE (0.52%), RMSE (1.58)
[173] |  Bitcoin |  N-N |  Time Series Analysis: Vector Autoregression |  P-Value
[166] |  Bitcoin |  Y-Y |  Regression: Graph Chainlets, RF |  RMSE
[167] |  Bitcoin |  N-N |  Regression: Bayesian Regression, GLM/RF |  Unclear
[168] |  Bitcoin |  N-N |  Regression and Deep Learning: GRU, GRU-Dropout, GRU-Dropout-GRU, LSTM, Neural Network |  RMSE (0.017)
[183] |  Bitcoin |  N-N |  Deep Learning: CNN |  Unclear
[172] |  Bitcoin |  N-N |  Time Series Analysis: OLS |  p-Value
[189] |  Bitcoin |  Y-N |  Unclear: Full connected NN , Multi-Window Prediction Framework, SVM |  MAPE (1.69%)
[187] |  Bitcoin |  N-N |  Classification and Regression: DCN, DNN, Hybrid Model |  Loss Ratio (0.0), MAE (9.96% - 1707.42), ROI (11%), Sharpe ratio (1.03), Total profit (USD 65,043.99), Volatility (0.07), Win Ratio (1.0), Win–loss Ratio (-)
[178] |  Bitcoin |  N-N |  Classification: DT, KNN, Linear Discriminant Analysis, LR, Quadratic Discriminant Analysis, RF, SVM, XGBoost |  Accuracy (0.648), Confusion Matrix, F1 (0.63), Precision (0.71), Recall (1.0)
[179] |  Bitcoin |  Y-Y |  Classification and Deep Learning: CNN, CNN-GRU, CNN-LSTM, Complement NB, DT, GRU, LR, LSTM, RF, SVM |  Accuracy (0.6086), F1, Precision (0.60), Recall (0.60)

###  6.4 Performance Prediction

Seven papers in this study addressed a performance prediction problem, using a
classification task [192, 193, 194], a regression task [54], a classification
and deep learning tasks [195, 196], or a time series analysis [197].

Two papers proposed a solution for gas price prediction on Ethereum. [196]
used the gas price spanning from October 10, 2020 to October 24, 2020, while
[195] used gas-related features spanning from March 17, 2022 to April 13,
2022.

The five other papers focused on the transactions: transaction throughput
prediction on Hyperledger Fabric [54], transaction confirmation time
prediction on Ethereum [192, 193, 194], and transaction fees prediction [197].
In order to address these topics, the authors used: transaction latency, send
rate, transaction throughput, and error [54]; gas-related features and
transaction-time related features [192]; transaction-specific features, block-
and network-specific features [193]; and cryptocurrency prices-related
features and block- and network-specific features [197].

All datasets used in these papers consisted of at least more than 10,000 data
points. They covered various periods, ranging from before November 2018 until
April 2022. August 2019.

Table 12: Papers Addressing Performance Prediction Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[54] |  Hyperledger Fabric |  Y-N |  Regression: ANN |  MAD (0.250), MSE (6.701), RMSE (2.716)
[192] |  Ethereum |  N-N |  Classification: MLP, NB, RF |  Accuracy (0.8361), Cohen’s Kappa (0.7877)
[197] |  Ethereum |  Y-N |  Time Series Analysis: Granger causality |  p-Value
[193] |  Ethereum |  N-N |  Classification: Ensemble Approach, KNN, MLP, RF, SVM, |  Accuracy (0.9018), F1 (0.897), Macro F1, Precision (0.896), Recall (0.896), ROC (0.936)
[195] |  Ethereum |  N-N |  Classification and Deep Learning: LSTM, XBoost |  MAE (0.064), RMSE (0.108), R2 (0.962)
[196] |  Ethereum |  Y-Y |  Classification and Deep Learning: GRU, LSTM, Prophet, RNN |  MAE (0.063), MSE (0.008), R2 (0.896), RMSE (0.088)
[194] |  Ethereum |  Y-Y |  Classification: DT, LR, RF, SVM |  Accuracy (0.98272), AUC-ROC (0.873668), F1 (0.674454), F2 (0.617612), Matthews Correlation Coefficient (0.702714), Precision (0.797683), Recall (0.973890), TNR (0.995), TPR (0.974)

###  6.5 Smart Contract Vulnerability Detection

In our studies, we found twelve papers addressing smart contract vulnerability
detection, assessment or classification on Ethereum. All papers used
classifiers and/or deep learning models and reported various performance
measures.

In the papers in our study, the authors experimented with a set of classifiers
and/or deep learning algorithms: (i) DR-GCN, Eth2Vec, MODNN, SoliAudit [198];
(i) CNN, DT, KNN, LR, RF, SVM [199]; (ii) DT, NN, RF, SVM [200]; (iii)
Vanilla-RNN, LSTM, BLSTM, BLSTM-ATT [201]; (iv) KNN, RF, SVM [202]; (v)
AdaBoost, KNN, RF, SVM, XGBoost [203]. Also, multiple authors proposed a novel
classification algorithm: the Average Stochastic Gradient Descent Weighted
Dropped Long Short Term Memory [204], Bytecode matching [205], Peculiar [206],
DeeSCVHunter [207], SmartEmbed [208], and Eth2Vec [209]. They usually compared
the performance of their solution with baseline models.

As far as the features fed to the models are concerned, the opcode is a
popular feature [198, 199, 204]. Furthermore, the authors in [200] extracted
17 features from the smart contract code: (i) features representing the
execution path, and (ii) features representing heuristic guesses of the
complexity of the code. In [209], the authors used the same features, except
for the hexadecimal address. Other features used in the papers we studied
include: contract snippets [201], bigram features from simplified opcodes
[202, 203], bytecode [205], crucial data flow graph [206], and vulnerability
candidate slice [207].

Except for the study by [201], all papers used datasets with more than 10,000
data points. Also, no authors reported a dataset spanning after 2018.

Table 13: Papers Addressing Smart Contract Vulnerability Detection Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[198] |  Ethereum |  Y-Y |  Classification and Deep Learning: DR-GCN, Eth2Vec, MODNN, SoliAudit |  Accuracy, Euclidian Distance, F1 (0.9628), FN, FP, Fowlkes and Mallows Index, Loss function, Precision (0.9651), Recall (1.0), TN, TP
[204] |  Ethereum |  Y-N |  Deep Learning: Average Stochastic Gradient Descent Weighted Dropped Long Short Term Memory |  Accuracy (0.913), F1 (0.952), Precision (0.944), Recall (0.979)
[205] |  Ethereum |  N-N |  Classification: Bytecode matching |  FNR (0.1429), FPR (0.0), Precision (0.9195)
[199] |  Ethereum |  N-N |  Classification and Deep Learning: CNN, DT, KNN, LR, RF, SVM |  Accuracy (0.973), F1 (0.904), Precision (0.929), Recall (0.882)
[200] |  Ethereum |  N-N |  Classification: DT, NN, RF, SVM |  Accuracy, F1, FN, FP, Precision, Recall, TN, TP
[201] |  Ethereum |  N-Y |  Classification and Deep Learning: BLSTM, BLSTM-ATT, LSTM, Vanilla-RNN |  Accuracy (0.8847), AUC, F1 (0.8826), FPR (0.0857), Precision (0.8850), Recall, ROC Curve
[202] |  Ethereum |  N-N |  Classification: KNN, RF, SVM |  F1 (0.98), Macro-F1 (0.9303), Micro-F1 (0.9698), ROC
[203] |  Ethereum |  N-N |  Classification: AdaBoost, KNN, RF, SVM, XGBoost |  F1 (0.99), Macro-F1 (0.9641), Micro-F1 (0.9798), ROC
[206] |  Ethereum |  Y-Y |  Classification: Peculiar |  F1 (0.9210), Precision (0.9180), Recall (0.9240)
[207] |  Ethereum |  Y-N |  Classification: DeeSCVHunter |  Accuracy (0.9302), F1 (0.8687), Precision (0.9070), Recall (0.8346)
[208] |  Ethereum |  Y-Y |  Deep Learning: Sequence to sequence recurrent autoencoder |  -
[209] |  Ethereum |  Y-Y |  Classification: Eth2Vec, NNN, SVM |  F1 (0.575), Precision (0.77), Recall (0.507)

###  6.6 Other

We found three papers that do not fit the identified categories above.
Specifically, [49] addressed behavior pattern clustering. The authors used a
dataset of less than 2,000 data points. Next, [50] proposed a solution for
smart contracts labeling on Ethereum, with a dataset of less than 1,000 data
points. The authors used k-medoids and Affinity Propagation algorithms to
obtain seven clusters. Finally, in [51] , the authors applied a clustering
algorithm on a dataset of less than 100,000 data points. The goal was to
perform some data analysis on the EOS blockchain.

Table 14: Papers Addressing Other Use Cases Paper |  Blockchain |  Data-Code |  Model |  Performance
---|---|---|---|---
[49] |  Unclear |  N-N |  Clustering: BPC, BPC with Random Cluster Center Initialization |  Precision (0.7426)
[50] |  Ethereum |  N-N |  Clustering: Affinity Propagation, k-Medoids |  Frequency distribution
[51] |  EOSIO |  Y-N |  Clustering |  Average Shortest Path Length, Clustering coefficient (0.448), Degree Distribution

##  7 Discussion & Conclusion

###  7.1 Major Findings

In this work, we conducted a systematic mapping study on Machine Learning
techniques applied to blockchain data. We analyzed 159 articles, selected and
classified them according to various dimensions. In order to perform this
study, we followed the most widely accepted guidelines in the literature and
gave an overview of the current research in the given area. This structured
methodology allowed us to extract key information and address the four main
research questions. We also identified the gaps in the existing research works
and recognized future research directions.

In this study, we selected the most relevant and important publications by
applying inclusion and exclusion criteria in a process consisting of 6 phases,
while querying in the most widely used scientific databases in these fields
i.e. Google Scholar, Springer, and ScienceDirect. The publication forums of
the studies, as well as the distribution of the studies by publication type
were extensively analyzed.

Followingly, the studies were classified according to four different
perspectives:

  1. 1.

Domain & Use Case with the major use cases being address classification,
anomaly detection, cryptocurrency price prediction, performance prediction,
smart contract vulnerability detection. The majority of the studies we
considered here addressed the problems of anomaly detection.

  2. 2.

Blockchain Platform with the majority of studies focusing on Bitcoin and
Ethereum data, while some research teams focused on a combination of
blockchains.

  3. 3.

Data with detailed analysis on the blockchain and external data sources as
well as the size of datasets examined by the different studies to draw
interesting results. It was inferred that the majority of studies were based
on datasets of more than 1,000,000 data points and while some datasets were
made available by the authors or were available by default, the majority of
studies did not share the data they used.

  4. 4.

Machine Learning Models applied to blockchain data such as classification,
clustering, deep learning, graph learning, regression, time series analysis,
or a combination of the above, with classification being the most widely used
technique. Regarding the machine learning workflow followed in the different
studies, some preprocessing techniques were commonly employed such as data
preprocessing, feature extraction, and algorithm selection. On the subject of
the specific algorithms, Random Forest and the Support Vector Machine were the
most favored ones and the most commonly utilized evaluation metrics across
studies were Accuracy, Precision, Recall, F-Score, and ROC Curve.

###  7.2 Challenges and Future Research Directions

As blockchains are increasingly popular, their size increases significantly
depending on the number of transactions, the block size, the frequency of the
block creation, and the additional data stored e.g. Ethereum smart contract
events and metadata. Larger data size, which currently exceeds hundred of
Gigabytes for the most popular blockchains, pose challenges in terms of
storage, and computational requirements in applying machine learning to
blockchain data and fully leverage the potential of machine learning
algorithms in this context. Furthermore, to exploit the significant amount of
valuable information stored in the different smart contracts and Decentralized
Applications requires an additional notable effort to preprocess and often to
develop solutions in order to extract it from blocks and transactions. This
challenge requires further attention and it is indicative that there is an
initial effort in this direction for Ethereum DApps [119] and DeFi [210] with
many potentials by taking also into consideration the interoperability between
different blockchains and external data sources.

While some research works such as [116] [135] published their underlying
datasets and acted as a reference point and a comparison baseline, most works
are not making their dataset/code available and as a result, there is a lack
of standardized evaluation frameworks and benchmarks. An additional challenge
would be the constant update of such benchmarks with the latest block data and
the domain’s changing patterns. As an example in the domain of Anomaly
Detection new malicious accounts, illicit transactions, Ponzi schemes based on
smart contracts, and phishing activities constantly emerge, based on novel
patterns and mechanisms and the efficient proposed algorithms should be able
to identify the most recent malicious activities. This lack of data sharing is
a common problem in academia, and many reasons explain this issue, such as
insufficient time, lack of funding, lack of skills, or authors’ attitudes.
Various mechanisms could be put in place in order to promote or facilitate
data sharing, including: explicit data sharing policies from journals, support
from authors’ institutions, better infrastructure/data repositories, or
incentives for sharing [211, 212, 213, 214].

Finally, in relation to the points raised above, blockchain is a fast evolving
technology: new blockchains, new protocols, new DApps, new use cases, and new
interactions with other technologies (blockchain and IoT, blockchain and AI)
emerge constantly. Being able to store and process the growing volumes of data
is crucial, as well as being able to compare past phenomena with the current
ones. The latter would be made possible with proper data sharing.

###  7.3 Conclusion

In summary, this study provided detailed insights into the research works on
applying machine learning to blockchain data. As the adoption of blockchains
continues to grow, new technical solutions are integrated and new services
emerge. Future research needs to take into consideration such aspects in order
to better understand the research landscape. Novel research is needed to
propose standardized frameworks across the different use cases analyzed in the
context of this work. As significant growth in machine learning research is
noticed and the proposal of innovative ML algorithms is observed, future
research is also required to explore their tailoring, customization and
potential for the unique features of blockchain problems and data.

## Acknowledgment

This work was supported by J.P.Morgan Faculty Research Awards 2022 and NSF
1932220.

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