# Generative Adversarial Networks for Malware Detection: a Survey

Aeryn Dunmore1 JJJJulian Jang-Jaccard1 FSFariza Sabrina2 JKJin Kwak3
Cybersecurity Lab, Massey University, Auckland, NZ  School of Engineering and
Technology, Central Queensland University, Sydney, NSW, Australia  Department
of Cyber Security, Ajou University, Suwon, Republic of Korea

###### Abstract

Since their proposal in the 2014 paper by Ian Goodfellow
goodfellow2014generative , there has been an explosion of research into the
area of Generative Adversarial Networks. While they have been utilised in many
fields, the realm of malware research is a problem space in which GANs have
taken root. From balancing datasets to creating unseen examples in rare
classes, GAN models offer extensive opportunities for application. This paper
surveys the current research and literature for the use of Generative
Adversarial Networks in the malware problem space. This is done with the hope
that the reader may be able to gain an overall understanding as to what the
Generative Adversarial model provides for this field, and for what areas
within malware research it is best utilised. It covers the current related
surveys, the different categories of GAN, and gives the outcomes of recent
research into optimising GANs for different topics, as well as future
directions for exploration.

Generative Adversarial Networks (GAN),

machine learning,

current research survey,

threat detection,

malware,

data augmentation,

adversarial examples,

###### keywords:

Research

1Cybersecurity Lab, Massey University, Albany, New Zealand
2School of Engineering and Technology, Central Queensland University, Sydney,
NSW, Australia
3Department of Cyber Security, Ajou University, Suwon, Republic of Korea

##  1 Introduction

Generative Adversarial Networks, or GANs, are a type of deep learning neural
network model based on the Game Theory premise of a zero-sum
gamegoodfellow2014generative . These networks have become popular in many
fields as a Machine Learning (ML) model which has great success at
synthesising large samples of dataset classes based on learning classes and
features from an existing dataset. They are particularly good at synthesising
images Jang20201 , making them both popular in computer vision tasks, and
excellent at generating malware ’images’ for training systems to detect
malicious files and applications. They also offer a chance to augment the
rarest classes in a dataset Pham.2021 . While they have received attention
from many disciplines and research topics, the research into GANs for
synthesising images of different malware classes is very promising, and as
such, is where we have chosen to focus our survey.
The research in this study concerns of the state of the art in GANs and
Malware, and we have found a space for an up-to-date examination of where this
research discipline is and where it appears to be headed. As is explained in
Section 3, on related works, while there are surveys or studies which are
similar in aspects of their research, to our knowledge there is no updated
survey on this topic. This is of distinct importance because of how much
growth we have seen in this area of research in recent years. We have done our
utmost to present a balanced examination, with both breadth and depth, which
can be of use to both researchers new to the area, and those wanting an update
on the current problem space. We have also attempted to approach this survey
in a way that makes it accessible for the machine learning or cybersecurity
researcher both.
The rest of the paper is structured as follows: Section 2 describes the
structure of GAN models, and how they are built and trained, as well as
defining malware for the purposes of this paper. Section 3 gives a relatively
brief breakdown on the recent works with the most similarities to our survey,
and explains how we have developed something different in content and
structure. Section 4 clarifies the different methods by which researchers
measure the performance of their respective GAN models. Section 5 explains the
datasets used in the experiments and research we have surveyed and what types
of data they contain as well as their origins. Section 6 delves into the
different types of Generative Adversarial Network - both the most commonly
implemented models and the innovations that have come from recent researchers
developing new ways in which to use the model. Section 7 will explain the
types of uses GAN models are being used in malware research, along with the
specific areas within the area to which GAN research is contributing. Section
8 contains our in-depth discussion of how GANs are functioning within malware
research and what this means for researchers both in cybersecurity and machine
learning. Finally, Section 9 discusses the opportunities for future research,
and then Section 10 goes on to the conclusions we believe can be drawn from
the survey of papers within this discipline.

##  2 Terms, Definitions, and Explanations

A Generative Adversarial Network, at its base, is a machine learning algorithm
built out of two separate deep learning networks which work together,
competing to win a zero-sum game. One network takes in noise and then attempts
to create samples with the right characteristics to have them seem like real
or ’genuine’ samples. The second network takes as input both real and
generated samples, and then classifies them as either real or fake samples.
The back-propagation that occurs then is the backbone of the model. If the
Discriminator network is right, information is sent back to the Generator
network, so that it will adjust its weights and probability distributions to
improve the quality of its forgeries. If the Discriminator instead gets it
wrong, that information is sent to the Discriminator to make the necessary
adjustments. The games end when the Discriminator has the accuracy of a coin
flip - when the forgeries are all but impossible to separate from the genuine
samples.
As discussed above, the Generator creates data that is meant to look as real
as possible. The Discriminator has only one job - determine if the data
provided to it is generated data (created by the Generator) or if it is in
fact genuine data. The Generator is considered to have "won" when the
Discriminator has a success rate of 50%. Meaning that the Generator is so good
at producing almost real data, that the Discriminator is left with the same
accuracy as tossing a coin. The GAN is considered an unsupervised machine
learning method, and it was developed in 2013 to help model the behaviour of
wildlifeAggarwal.2021 . GANs are an alternative generative model to
Variational Autoencoders (VAE), which can also be used to create new samples
from a given dataset. In many of the papers we surveyed, VAE were used as a
point of comparison/control in the experiments for improved GAN models.

###  2.1 How does a GAN model work?

Having given a simplistic overview, we now explain the architecture of the GAN
model for machine learning in detail. The architecture is innovative for the
way it processes and creates both information and datasets. In a world where
we need exceptionally large datasets to train the machine learning algorithms
that are now slipping into so much of our technology - and therefore into our
lives - the ability to create new data for training purposes is invaluable.
This is, of course, provided the data is, or is at least almost, authentic.
Creating exceptional forgeries, in the way that GANs do, is therefore a reason
in and of itself to employ them in most problem domains. Cybersecurity
especially has need of as many samples as possible of the different types of
malware, in order to be able to train defensive technologies, to detect when a
file or action is malicious.

Figure 1: A standard GAN, SGAN, or LSGAN model, reproduced based on diagrams
from Wang et al., 2017 wang2017generative

###  2.2 A Standard GAN Model Structure

The standard/vanilla Goodfellow GAN design is simple but ingenious. It relies
on the use of two main deep learning neural nets, with back-propagation and
feedback. The Generator and Discriminator play a "two-player minimax game"
goodfellow2014generative  using the value function found in Equation 9. This
equation is taken from Goodfellow’s original 2014
papergoodfellow2014generative  introducing Generative Adversarial
Networksgoodfellow2014generative .
Figure 1, from wang2017generative , shows the structure of a standard model
GAN, and one of its derivatives, Least Squares GAN (LSGAN) discussed later in
Section 6.8.

####  2.2.1 Generator

The Generator portion of the network is arguably the more complex. The
Generator Network within the GAN model starts with a random seed or noise as
input, and produces an output which starts understandably far from the goal.
However, as the Discriminator Network feeds information through back-
propagation to the Generator, it slowly achieves convergence on the target
samples. Each iteration, each epoch, the Generator syntheses data that is more
and more realistic. Convergence and training of the Generator Network is
finished when the Discriminator cannot tell if the synthesised data is real or
manufactured with any more than 50% accuracy - effectively becoming as useful
in classification as a coin toss. The advantages to being able to do this are
many, and this generation of data is what makes the GAN model so popular in
the world of machine learning. The ability to extract and analyse important
features of each dataset class are a highly sought after trait in machine
learning research.

####  2.2.2 Discriminator

The Discriminator’s job essentially comes down to a binary option. Is the data
real, or is it synthesised? This part of the network, like most deep neural
networks, is about feedback - in this case, the back-propagation of the result
to the Generator Network. When the Discriminator guesses incorrectly, that
alters the internal weights of the system as the information back-propagates
to both Generator and Discriminator. The Discriminator is also playing to
lose, as the target is to have the Discriminator as effective at
distinguishing real data from generated data as a simple coin toss. At that
point in the game, the Generator has graduated to building data for use in
other scenarios. The data provided is considered so close in feature-space
that it may as well be treated like the real deal. The Discriminator is the
part of the dual network that recognises when the model has been sufficiently
trained to produce high-quality synthesised data. It is, also, no longer
required once the Generator is producing the samples at that level of
’perfection’. It does not need to be used outside of the training of the
Generator, as that is its singular purpose.

####  2.2.3 Types of feedback loops

Essential to every GAN implementation is the type of feedback response network
it utilises. Being able to feed the results of the tests run by the
Discriminator back to the Generator is key in the development of an effective
Generator. With every wrong choice, the Generator gets to adjust the weights
that little bit closer to where they want to be. Likewise, the Discriminator
choosing correctly means that the Generator needs to update its neural weights
differently, to change the emphasis on particular neurons, and to change the
outputs of the network for a more favourable outcome. The back-propagation of
the network allows both systems to carefully adjust the weights and
probabilities of their internal deep learning neural networks. Because of the
feedback loop needed by GAN architecture, it is a back-propagation network.
There are networks that change the types of feedback loops and what is
presented to cause changes in the algorithm. The function to alter the weights
is essential to the training period, and is as such customised in different
models and applications such that the network can reach optimal function as
efficiently as possible. It is important to remember that this adjustment over
time occurs in a black-box. The results and inputs are what we as researchers
can control, while the actual operations can only be modelled and estimated.

###  2.3 Malware

Malware, in the general definition for the purposes of this paper, is code
written to cause malicious behaviour. Trojans and ransomware are both examples
of malware. In its purest form, malware is programming intended to in some way
break or disrupt the regular operation of an operating system Amin.2022 . One
of the most well-known malware attacks is WannaCry’s ransomware in 2017,
infecting computers across the globe with hackers locking users out of their
PCs. As ransomware has evolved, it has become a billion dollar business
O'Kane.2018 . This is simply another reason to use every means at our disposal
to create a new generation of detection systems, such that individuals (who
may be mostly unaware of how best to protect themselves) need not attempt to
protect their devices on their own. This paper aims to show the ways in which
GAN models can and are being used in order to help create these new systems.

##  3 Related Work

The areas in which the use of GAN is both possible and implemented is
exhaustive. The classification, generalisation, and feature extraction
abilities of the GAN models make them useful in too many fields to reasonably
keep track of, but there are many surveys that have tried to enumerate all the
ways in which GAN models helped in their fields. These include steganography
Tang.2017 , the cracking of cryptographic methods Corley.2019 , e-commerce
Kumar.2018 , and cross-lingual methods for detecting hackers 7y . There are
however, some areas in which GAN models are more common than others. We have
summarised the types of GAN models examined in the related works in Table 1.

In Berman et al., 2019Berman2019 , the authors take the chance to explore the
ways in which deep learning methods have been integrated into different realms
of cybersecurity. Our paper is similar, though it focuses specifically on the
work done using Generative Adversarial Networks for implementation in the
research space of malware, both generation and detection. Berman et al.
Berman2019  is intended to familiarise the reader with research into machine
learning for cybersecurity. The authors are careful to emphasize the
difference between the deep versus shallow neural network machine learning
models. This survey is far greater in depth than spread - the types of uses
for GAN models in the survey are a small number primarily about identifying
attacks, but they are all examined in great detail over the course of many
papers in each area. Their survey is indeed intended to familiarise the reader
first with machine learning, neural networks, and deep learning, before moving
on to Generative Adversarial Networks and then to their applications in
cybersecurity. In this, the paper does achieve its aims. However, the lack of
variety in the tasks in which GAN models can be used in cybersecurity is a
note on which there is certainly room for more breadth. This survey is very
careful to ensure the reader understands the metrics, and the results for each
of the different papers included in the survey are clearly outlined. The
metrics are important and effective at communicating why researchers should
further investigate GAN methods for different tasks.

In Liu, et al., 2022, Liu2022123 , the authors undertake a thorough,
extensive, and careful examination of the state of the art in Adversarial
Machine Learning - of which GANs are one type. The stated purpose of this
survey is to examine the weaknesses of Machine Learning Intrusion Detection
Systems, and look at ways AML models are being implemented to assist in
defending them. The over-arching theme, of course, is through the creation of
adversarial examples such that Machine Learning approaches can train on unseen
data, and learn to spot the necessary contextual and semantic relationships
that can signal malicious code.
Like our paper, it includes a table of related and similar works and their
main features. The paper leans heavily on the uses for machine learning to
protect mobile networks from attacks. Interestingly, this paper separates into
two distinct parts with regards to the surveying of papers, coded as the
Offensive Perspective, and the Defensive Perspective.
The focus on Adversarial Machine Learning, as opposed to Generative
Adversarial Networks, for network intrusion detection systems mean that, while
this paper is very in-depth and covers a variety of machine learning models
including GANs, our paper fits within the gap between AML for IDS and GANs for
malware research.

The survey, Navidan et al., 2021 Navidan.2021 , covers familiar ground in the
research surveyed, but in exceptional depth. The authors are quick to note
that, as is evident by the content of these surveys, while GAN for
cybersecurity is a relatively new field, it is already being extensively
researched. However, this paper has a very small related works section, with
only two other surveys mentioned briefly.
The survey covers some interesting areas in which GANs are currently being
utilised. In the paper li2019dynamic , the survey authors note the creation of
an ingenious GAN model for morphing traffic flow data, called FlowGAN. The
purpose of this model is to train it as to what benign or normal traffic flow
patterns look like. Then it morphs the traffic data that needs to pass by
undetected, into something with the right features and patterns to be labelled
as benign/normal traffic. Such a model would be of interest in regards to
malware research as a method to defend against malicious files and
applications being disguised as normal and benign traffic.
In Future Work, the authors of the survey note that improving ways to avoid
image translations are of great significance. This would ease the ability of
researchers when finding or developing a new method to use with a different
type of data, making it not so cost-expensive with regards to overhead.

We have found a space within these existing surveys to fill gaps with regards
to how Generative Adversarial networks are used in areas of malware research
for cybersecurity. We have done so with a focus on creating an overview that
will be of use to those individuals in need of a primer on GANs and their
potential applications in research into malware. We have attempted to balance
depth with breadth of content, and to point readers to other papers that may
give them further information on the use of GAN models in these research
areas. While our paper might be considered of a parallel topic to Arora2022433
, the latter paper is much more compact, and as well as dealing more broadly
in cybersecurity as a whole, it involves a detailed case study which leaves it
little room to discuss the topics in the depth which we have attempted in this
survey. Overall, we believe that we have contributed valuable commentary on
the state of the art in GAN models for malware research.

Topics | Berman et al., 2019Berman2019  | Liu, et al., 2022Liu2022123  | Navidan et al., 2021Navidan.2021
---|---|---|---
Malware | X | X | X
Adversarial Examples |  | X | X
Data Augmentation | X | X | X
Network Data | X |  | X
Reinforcement Learning |  | X |
Unseen Examples |  | X |
Offensive/Attacker Models | X | X | X
Defensive/Defender Models |  | X |
Social Network Analysis | X |  |
Android Malware |  |  | X
Financial Fraud Detection |  |  | X
Image Enhancement | X |  |
Domain Generation Algorithms | X |  |
Botnet Detection | X |  |
Drive-By Download Attacks | X |  |
Password Attacks |  |  | X
Mobile Network Attacks |  |  | X
Internet of Things Attacks |  |  | X
GAN MODELS DISCUSSED IN SURVEY
VanillaGAN | X | X | X
CGAN |  |  | X
DCGAN | X |  | X
WGAN |  | X | X
BiGAN |  |  | X
CycleGAN |  |  | X
AC-GAN |  |  | X
MalGAN |  | X |
ISGAN |  |  | X
InfoGAN |  |  | X
FlowGAN |  |  | X
Table 1: Types of GAN Research in Related Works

##  4 Measuring Performance

In most papers related to GAN schemes, there are expected metrics for
evaluating a machine learning systems like this oneeberhart1990performance .
The most popular are listed here, as are the ways these show the performance
of the GAN.

###  4.1 Evaluation Metrics

The TP, FP, TN, FN scores are often tabulated as a Confusion Matrix to show
the performance of the ML algorithm. An example Confusion Matrix can be found
in Table 2.

Predicted Classification
---
|  | Benign | Malicious
Actual Classification | Benign | TP | FP
Malicious | FN | TN
Table 2: Metrics in Machine Learning Classifiers

####  4.1.1 True Positive

The True Positive/TP is the number of correctly predicted positive results, or
the total number of correctly classified benign samples.

####  4.1.2 False Positive

The False Positive/FP is the number of incorrectly predicted positive results,
or the total number of incorrectly classified benign samples.

####  4.1.3 True Negative

The True Negative/TN is the number of correctly predicted negative results, or
the total number of correctly classified malicious samples.

####  4.1.4 False Negative

The False Negative/FN is the number of incorrectly predicted negative results,
or the total number of incorrectly classified malicious samples.

####  4.1.5 Accuracy

The accuracy is the average of correct predictions - of both positive and
negative varieties - when classified. Thus, it is the correct predictions
divided by the total predictions, or:

$acc=\frac{TP+TN}{TP+TN+FP+FN}$ (1)

This is the assumed metric in papers or articles which talk only about
averages and score.

####  4.1.6 Precision

Also known as Positive Predictive Value or PPV, this is the samples that were
classed correctly as benign over all samples that have been classified as
benign.

$p=\frac{TP}{TP+FP}$ (2)

####  4.1.7 Recall

The recall, also known as true positive ratio, or sensitivity, is the ratio of
samples classed as benign over the total samples classed as benign.

$r=\frac{TP}{TP+TN}$ (3)

####  4.1.8 F1-Score

This is the Harmonic Mean of the precision and the recall values. A harmonic
mean is one of three types of Pythagorean averages. It is heavily influenced
by the lowest of the values, when applied to real numbers, meaning it holds an
important place to check the minority classes’ accuracy.§

$F_{1}=2\left(\frac{pr}{p+r}\right)$ (4)

####  4.1.9 Inception Score

When $g$ is the Generator, $d$ is the Discriminator, and there are two finite,
label sets, $\Omega_{X}$ and $\Omega_{Y}$. As such, $p_{g}$ is a distribution
over $\Omega_{X}$. $p_{Y}:\Omega_{x}\rightarrow M(\Omega_{Y})$ is the
discriminator function and $M(\Omega_{Y})$ is the set of all possible
probability distributions over the set $\Omega_{Y}$. Any image can be $x$,
while $y$ is any label. Thus, writing $p_{d}(y|x)$ is calculating the
probability that the image $x$, has the label $y$ \- as calculated by the
Discriminator Network. The below shows the equation for calculating the
Inception Score over all probability distributions, $\Omega_{X}$, and
$\Omega_{Y}$Barratt.2018 .

$IS(g,d)=\exp(\mathbb{E}_{x}\sim p_{g}[D_{KL}(p_{d}(\cdot\mid x)\parallel\int
p_{d}(\cdot\mid x)p_{g}(x)dx)])$ (5)

There is a pre-trained network which measures the Inception Score, and the
higher the score of the model, the higher the quality of the images produced
salehi2020generative . The Inception Score and Network were introduced in 2016
for Convolutional Neural Networks by Szegedy et al. szegedy2016rethinking . It
was originally developed to remove human subjectivity in computer vision
research.

####  4.1.10 Mode Score

The Mode Score is meant to be an improved version of the Inception Score. It
still measures the quality and diversity of images, but it counts the prior
distribution of labelsBorji.2018 .

$MS(\mathbb{KL)}=\exp(\mathbb{E}_{x}[\mathbb{KL}(p(y\mid x)\parallel
p(y^{train})]-\mathbb{KL}(p(y)\parallel p(y^{train})))$ (6)

####  4.1.11 Fréchet Inception Distance

There is another equation derived from the Inception Score. The Fréchet
Inception Distance (FID) and the Inception Score (IS) together can be used as
an attempt to solve overfittingiiiOverfitting occurs in statistical analysis
when too few samples are present and the model is fitted too closely to this
small selection of samples, making its ability to generalise low.. The FID is
shown below in Equation 7. The purpose of the FID is to examine the distance
between groups. It was also developed for the specific task of image
processing in machine learning szegedy2016rethinking . Frechet Inception
Distance for any two probability distributions, $\mu$ and $\nu$, over the set
of real numbers, $\mathbb{R}^{n}$, is calculated as follows:

$d_{F}(\mu,\nu):=\left(\underset{\gamma\in\Gamma(\mu,\nu)}{inf}\int_{\mathbb{R}^{n}\times\mathbb{R}^{n}}\parallel
x-y\parallel^{2}d\gamma(x,y)\right)^{1/2}$ (7)

The set used in the FID here, $\Gamma(\mu,\nu)$ is actually the 2-Wasserstein
distance over $\mathbb{R}^{n}$Borji.2018 . There is a second calculation for
the FID score, but it works only over two Gaussian, multi-dimensional
distributions, $\mathcal{N}(\mu,\sum)$ \- symbolised below as $r$ \- and
$\mathcal{N}(\mu^{\prime},\sum^{\prime})$ \- shown below as $g$.

$FID(r,g)=\parallel\mu_{r}-\mu_{g}\parallel_{2}^{2}+Tr(\sum_{r}+\sum_{g}-2(\sum_{r}\sum_{g})^{1/2})$
(8)

##  5 Dataset

The different datasets on which the Machine Learning algorithms are trained
have a significant effect on how they read the given data, what their biases
or preconceived ideas may be, and how they are trained to recognise different
integral features. In this section we have attempted to cover the main
datasets used in the papers we have surveyed.

###  5.1 DGArchive

The DGArchive is a set of domains, of 43 families, classes, or variants, with
more than 20 million domains as of 2015ploh15 . These domains are from models
in Domain Generating Algorithms which create domains for Control & Command
centres for botnets. The database of malicious botnet C&C domains allows for
machine learning classifiers to be trained on how to detect domain name
malware. This data is extremely important in creating new machine learning
methods for identifying botnet C&C centres (as in Choudhary.2019 ). The
compilation of this information into such a large and comprehensive database
is an important research tool. The DGArchive dataset is also used to create
adversarial machine learning models, such as MaldomDetectorAlmashhadani.2020 ,
which undertake the generation of malicious domain names itself, and allows
researchers to test defensive machine learning algorithms on an adversary.

###  5.2 VirusTotal

This repository of both anti-virus software and a database of files, both
benign and malicious, is known as VirusTotal. It can scan a given file using
70 antivirus systems as well as checking with URL and domain blacklisting
programsvirustotal22 . Each uploaded file - as well as resulting in a report
stating the findings and results labelling it either benign or malicious and
how these results were arrived at - is also kept and added to the overall
database of VirusTotal files. The service is free for research and non-
commercial use, and licences can be purchased for commercial users or those
needing a large sample set of dataAnderson.2018 . In addition to scanning,
users can request a subset of the database for use in training and testing
their own algorithms. This is a often used service in machine learning
research, such as Zhao.2018 , because of the depth and breadth of malware
covered by the VirusTotal dataset. It is also useful because of the constant
updating the servers get as users upload their own programs and files to scan.
This is an unusual dataset in that regard, where other datasets covered in
this section are static and set, while VirusTotal is continually changing and
updating. VirusTotal contains files, programs, Android applicationsCanbek.2018
, applications for Windows, Mac, Linux, iOS, and so on. This is another point
in favour of the database - the type of data available for training and
testing is extensive and covers a lot of ground, where other datasets
discussed only cover one type of information.

###  5.3 Contagio

Contagio is a publicly available dataset of malware, specifically samples of
Android malware and benign applicationsSingh.2021y8e . The dataset was updated
periodically between 2011 and 2018, and can be found for open access online at
a range of placesmilacontagio . The fact that this dataset is focused on
Android malware makes it extremely useful, as overall, openly available
databases of mobile malware are not as prevalent as those for desktop malware
or traffic flow data. As of 2021, Contagio contained 11,960 malicious and
16,800 benign samples of Android softwareSingh.2021y8e , with a total size of
approximately 9GB337 . This dataset can be accessed in .zip format for
researchers and white-hat activities.

###  5.4 Drebin

Drebin is a repository for Android malware, similar to Contagio. Drebin
contains 123,453 applications and 5,560 malicious APKs for Android, in a
variety of malware families, totalling about 6GB in size337 . It was collected
from 2010 to 2012. It was originally proposed as part of a paper in which
Drebin - a new algorithm - was proposed to catch malware on Android
smartphones. As part of this, a database of 5,560 malicious Android APKs were
collected, which now make up the Drebin dataset Arp.2014 . It is important,
therefore, to differentiate between the Drebin static-analysis detection
software, and the Drebin dataset. Both were organised around the use of eight
main feature sets for analysis Arp.2014 . These sets are as follows:

  * •

Hardware components

  * •

Requested permissions

  * •

App components

  * •

Filtered intents

  * •

Restricted API calls

  * •

Used permissions

  * •

Suspicious API calls

  * •

Network addresses

###  5.5 Comodo

The Comodo Databasecomodo2022 , maintained by Comodo Antivirus, contains
sample files of malware. As part of a program to encourage research, Comodo
partnered with universities and launched Comodemiacomodemia2022 . This gave
access to Comodo’s tools to researchers internationally, for research purposes
only. The Comodo malware database primarily contains files classified as
unknown malware - totalling 147,103 instances. The other categories are Trojan
viruses (462 instances) and Unwanted Applications (13 instances). It is a
clearly unbalanced dataset. However, like the other datasets, it is used to
train and test different machine learning classifiers, as in Ye.2018 .

###  5.6 VirusShare

The VirusShare database9vs  is a large online, open-source repository for
malware. A user account is required for access, but anyone with an account can
access and download the live viruses in the database. The database is found at
VirusShare.com, and is maintained by Corvus Forensics, though anyone can
submit files to be added to the dataset. Some researchers, like Duzgun.2021 ,
in which 14,616 unique examples were taken from the VirusShare database, have
taken portions of the VirusShare database and melded them with other datasets
in order to balance and augment datasets as necessary. In Menendez.2019 , the
VirusShare dataset was augmented with malware obtained from Kaggle, in order
to test the author’s proposed malware detection scheme, and was used as a
benchmark when the new model was run against VirusTotal’s (see Section 5.2)
antivirus detection program.

###  5.7 Microsoft Malware Classification Challenge (2015)

The Microsoft Malware Classification Challenge
https://doi.org/10.48550/arxiv.1802.10135 , made available an open source
database of malware examples for Windows. The challenge was part of a general
push to coders into creating their own deep learning methods for malware
classification Chivukula.2021 . It can be found primarily on
KaggleiiiiiiDataset and competition information can be found here:
https://www.kaggle.com/c/malware-classification. It was an open competition on
the site, for teams to come together and develop their own solutions to the
challenge. It was completed by 377 teams during the open competition time
between April 13 - 18, 2015. The dataset contains more than 20,000 malware
examples, and according to the authors of the challenge Ronen.2018 , as of
2018 it had been cited in over 50 research papers. It is now a widely used
dataset for machine learning research, as in 7856826 , 8268747 , Kalash.2018 .

###  5.8 MNIST

The MNIST dataset, or Modified NIST dataset, a collection of images of
handwritten characters, was introduced in 1998 by LeCun et alLecun.1998 , for
the primary purpose of computer vision and recognition tasks in machine
learning. It contains characters which are clear representations, as well as
those which have been perturbed to examine the extent to which a computer
vision algorithm can recognise deformed figures. Many models dealing with the
challenges of computer vision in machine learning have utilised this dataset.
For example, the InfoGAN model, discussed in Section 6.11, was trained and
tested on the MNIST dataset, Chen.2016 , before moving on to 3D renderings.
Another study, surveying the effectiveness of different models of GANs, used
the MNIST dataset to benchmark the performance of each model Cheng.2020 .
Since its inception, new, updated versions of MNIST have been proposed. One
such dataset is EMNIST (Extended MNIST), which takes the dataset from digits
only into handwritten alphanumeric characters Cohen.2017 , with a total of
814,255 samples in all classes combined.

##  6 Types of GAN models

The papers we have surveyed have used a range of variants of the traditional
GAN. As a reference and refresher, we have included this section. in which we
cover the different types of GANs we will be discussing, and the points of
difference in each. We also wanted to clearly illustrate the issues inherent
in the standard GAN model, so that the variations which are developed
specifically for overcoming them are understood.

###  6.1 Vanilla GAN

The traditional, or Vanilla, Generative Adversarial Network is the original
proposed model from Goodfellow et al’s 2014 paper goodfellow2014generative .
The authors proposed the GAN model as an alternative to Variational
Autoencoders for adversarial machine learning. This original version of the
Generative Adversarial Network is a deep learning model, and was based on
adversarial nets as a framework, with back-propagation. This model uses a two-
player minmax game to adjust the weights, as per Figure
goodfellow2014generative . An important distinction is that while the
discriminator has access to both real and generated data, the generator has no
access to either, and so has to rely on the value functions and the back-
propagation to change the weights and take the model closer to producing
realistic generated output Cheng.2020 . The generator and discriminator are
both able to be non-linear mapping functions Mirza.2014 . The GAN model came
through the adversarial nets framework, a way of dealing with weights without
Markov chains, and instead using back-propagationMirza.2014 . For an in depth
overview of how the Goodfellow GAN operates, please refer to Section 2.1.
Inherent Problems in the Goodfellow GAN Architecture

####  6.1.1 Mode Collapse Problem

The complexities of the MinMax game that are essential to the standard/Vanilla
GAN result in an optimisation problem. This is solved in the standard version
using the gradient descent-ascent (GDA) method Durall.2020 . However, this can
lead to serious errors in convergence resulting in failure of the GAN,
including a problem known as mode collapseThanh-Tung.2020 . Combating this
problem is one of the reasons there are so many variations of GAN models -
many are developed to help avoid the convergence problems in optimisation of
the minmax function as much as possible.

####  6.1.2 Catastrophic Forgetting

Catastrophic Forgetting (CF) occurs when knowledge of previously learned tasks
is abruptly destroyed by the learning of the current task (Thanh-Tung.2020 ).
CF can prevent proper convergence in the model, and limit it from finding the
necessary local maxima optimum for the task it is set. Remembering the
location and features of the real samples used to train the generator is
essential - when the generator loses these samples, catastrophic forgetting
occurs as the new generated samples are not created with the real samples as a
guideThanh-Tung.2020 .

It is important to note that mode collapse and catastrophic forgetting are
interlinked - they make the other worse in situations where both problems
arise. The equation for describing the optimal Discriminator in Goodfellow’s
GAN model is shown in Equation 9. The training criteria for a given
discriminator, $D$, and a generator, $G$, are shown in Equation 10.

$D^{*}_{G}(x)=\frac{p_{data}(x)}{p_{data}(x)+p_{g}(x)}$ (9)
$\displaystyle V(G,D)$ $\displaystyle=\int_{x}p_{data}(x)log(D(x))dx$ (10)
$\displaystyle+\int_{x}p_{z}(z)log(1-D(g(z)))dz$ (11) $\displaystyle V(G,D)$
$\displaystyle=\int_{x}p_{data}(x)log(D(x))dx$ (12)
$\displaystyle+p_{g}(x)log(1-D(x))dx$ (13)

###  6.2 Conditional GAN

The Conditional GAN (CGAN), proposed in Mirza.2014 , modify the original
vanilla GAN. In the original model, the generative process could not be
controlled or conditioned. It was unsupervised entirely. CGANs allow the
generation process to be controlled and directed, meaning that the model can
be steered towards a focus on a particular class, or feature. The original
paper proposing a CGAN model utilised the MNIST dataset (see Section 5.8) to
test its capabilities. The change in control occurs when the focus is put on
some element $y$, which can be a class, value, feature, so on, and this $y$ is
fed into both the generator and discriminator as an additional layer of input.
In the original proposal, the generator is fed not only the focus $y$, but
also a noise function, $p_{z}(z)$ Mirza.2014 . In a subsequent study, a CGAN
for facial recognition was proposed Gauthier.2014 , which used sampled random
noise for $p_{z}(z)$ and a random sampling for $y$ which is taken from the
training dataset, utilising a Pazan window, $p_{y}(y)$.

###  6.3 Deep Convolutional GAN

The Deep Convolutional Generative Adversarial Network, or DCGAN, was proposed
in a 2015 paper titled "Unsupervised representation learning with deep
convolutional generative adversarial networks" radford2015unsupervised . The
model for DCGAN was based in research around convolutional neural networks,
and how they might offer opportunities for growth in other machine learning
models. The paper was focused on the generation of sudo-natural images, as GAN
models are so highly effective in image generation tasks. While most deep
learning algorithms are black-box methods, it is possible through careful
tuning to examine the underlying functions of a Convolutional Neural Network
(CNN) model. The authors made use of several changes to traditional CNN
architecture, from the following papers:

  * •

Striving for simplicity: The all convolutional netspringenberg2014striving

  * •

Inceptionism: Going deeper into neural networks mordvintsev2015inceptionism

  * •

Batch normalization: Accelerating deep network training by reducing internal
covariate shiftioffe2015batch

###  6.4 cDCGAN

The conditional Deep Convolutional Generational Adversarial Network, or
cDCGAN, takes the properties of both the CGAN (See section 6.2) and DCGAN (see
section 6.3) models. It was proposed as part of a paper focusing on
handwritten Arabic characters Mustapha.2022 , a significantly more complicated
task than identifying English alphanumeric characters. Arabic characters are
distinctly different in that Arabic lettering can have similar characters to
the extent that they "are only distinguishable by dots"Mustapha.2022 . The
database utilised was the AHDBase/MADBase Abdleazeem.2008 , containing 70,000
digits, chosen because it was the database that best matched the MNIST
database of numeric handwritten digits. (5.8). The discriminator in the cDCGAN
model is a deep CNN. The Leaky activation function is used in this model. The
generator matches its picture quality to 32x32 pixels, with a LeakyReLu
function.

###  6.5 Bi-directional GAN

The Bi-directional Generative Adversarial Network (BiGAN) was proposed in a
2017 paper called "Adversarial feature learning"donahue2016adversarial .
Similarly to MGAN (see Section 6.15) this is a three party model, consisting
of an encoder, a generator, and a discriminator. The role of the encoder is to
map data $x$ to a latent space representation $y$. Donahue et al specify that
the encoder is taught to invert the generator, even though the modules do not
interact with one another or directly process the other module’s outputs.
The BiGAN model is meant to excel at tasks that involve semantic data and
representation. They are also an entirely unsupervised model in machine
learning. Interestingly, BiGAN was brought into the spotlight in
Bioinformatics in a 2021 paper titled "BiGAN: LncRNA-disease association
prediction based on bidirectional generative adversarial network"Yang.2021 ,
the BiGAN model proved highly effective. When compared against the three gold-
standard algorithms for detecting the "associations of IncRNA-disease
pairs"Yang.2021 , BiGAN achieved the highest scores, including 93.1% for the
AUC. That was several percentage points higher than the standard methods.
BiGAN models are now found in many different fields, including research into
malware.

###  6.6 MalFox

The creation of MalFox, a GAN model for creating attacks and new malware
Zhong.2020  gave an important and powerful tool to those testing or attacking
existing systems. MalFox is an amalgamation of parser, generator, and
discriminator layers, which takes as input Windows Portable Executable files,
or PEs, and outputs the same executables. This makes it a more practical tool
that the more common adversarial example generators which often take an image
created by a feature extraction process and don’t create functioning malware
in pre-approved file types. Since its inception, MalFox has undergone more
than one transformation, but even the original version shows the power of GAN-
based schemes for attack purposes. The initial experiments were used against
pre-trained classifiers – Decision Tree; Random Forest; Logistic Regression;
Support Vector Machine; Multi-Layer Perceptron; Vote; Long Short-Term Memory;
Bi-directional LSTM; LSTM Average; Bi-LSTM Average; LSTM Attention; and BiLSTM
Attention. The evasion rate – the percentage of times the program was
classified as benign by the systems - MalFox achieved was 99% minimum across
the board. This is a stunning display of the power of these schemes.
Furthermore, when tested against the open-access giant VirusShare, the
detection rate was only 29.7% on average. The evasion rate on the same was
averaged as 56.2%. MalFox and the experiments done show exactly how powerful
these schemes can be.

###  6.7 MalGAN

In 2017, the authors Hu.2017  created a GAN which proposed black-box
adversarial examples for attacking via Windows binaries. This scheme, called
MalGAN, is now widespread, with multiple different variants and branches of
development. The use of binaries, and portable executable files, is one of the
ways this is so successful at showing the potential of GAN-based attacks. The
authors of the original MalGAN were able to get the detection rate down to
almost zero. This clearly demonstrates the danger that is posed by GAN attack
systems. Since its inception, MalGAN has been modified and improved, under the
auspices of creating the best database for the training of robust detection
schemes. Wang2021Zhang  proposed using an LSGAN model to address weaknesses
like mode collapse. The model they propose still uses the MalGAN scheme, after
the use of the LSGAN method, which involves implementing a Least Square
function. Their purpose was making MalGAN more robust and avoiding the
potential fallout from limiter problems or mode collapse. The authors are
still focused on creating adversarial examples, though they focus more on
poisoning attacks as well as the traditional ‘perturbation’ attacks, which
change only a small portion of the code while still retaining the capabilities
or functionality of the original sample. They are not the only researchers to
build a version of MalGAN with Least Square functions to increase the
robustness of the function. Wang2021Mi  also suggested the use of a Least
Square function in order to minimize mode collapse. The authors of this paper
also changed up the activation functions, and added LeakyReLU to the mix. They
achieved an 85% success rate over seven different ML classifiers.

###  6.8 Least Square GAN

Mao et al propose the creation of a GAN variant called the Least Square GAN,
(LSGAN)Mao.2017 . Named for its innovation, the model uses the Least Squares
equation for the discriminator. This helps to minimise the Pearson divergence
of $X^{2}$. The Pearson $X^{2}$ Divergence is a variant of the $f$-divergence,
The LS function can help distance correctly classified samples from the
genuine data, improving the performance of the classifier, and thus increasing
the training level of the generator. The objective functions of the LSGAN
model are presented in Equation 14, The objectives functions for the LSGAN
model, from Mao.2017 .

$\displaystyle\underset{D}{min}V(D)_{LSGAN}$
$\displaystyle=\frac{1}{2}\mathbb{E}_{x\sim
p_{data}}[(D(x)-b)^{2}]+\frac{1}{2}\mathbb{E}_{x\sim
p_{z}(Z)}[D(G(z))-c)^{2}]$ (14) $\displaystyle\underset{G}{min}V(G)_{LSGAN}$
$\displaystyle=\frac{1}{2}\mathbb{E}_{x\sim p_{z}(z)}[(D(G(z))-c)^{2}]$ (15)

###  6.9 AC-GAN

The auxiliary classifier GAN (ACGAN) was proposed in 2016 by Odena et al
Odena.2016 . The ACGAN variant was proposed, at the time, for use in image
generation, but has since moved into other subjects, as many GAN models do due
to their easily transferable nature. The variant distinguishes itself by the
use of an auxiliary decoder network within the discriminator. As a result, the
algorithm can:

  * •

Give as output the label of the class for training samples.

  * •

Output a subset of the set of latent variables used to generate the samples.

According to Navidan et al. Navidan.2021 , the strongest point of difference
between the ACGAN model and the CGAN variant is that in order to determine
class labels, the CGAN relies on the conditioning of the generator. In
contrast, the ACGAN predicts class labels due to the auxiliary decoder
network. The way the ACGAN predicts class labels can be found in Equation 16.

$\displaystyle L_{S}$ $\displaystyle=E_{x\sim p_{data}(x)}[\log D(x)]+E_{z\sim
p_{z}(z)}[\log(1-D(G(z)))]$ (16) $\displaystyle L_{C}$ $\displaystyle=E_{x\sim
p_{data}(x)}[\log Q(c|x)]+E_{z\sim p_{z}(z)}[\log Q(Q(c|z))]$ (17)

###  6.10 IS GAN

The Identity-Sensitive Generative Adversarial Network was proposed and focused
on face photo-sketch synthesis Yan.2021 . This is the process by which a photo
of a face is turned into a sketch through machine learning. The goal of
creating the ISGAN model was to create a formal image translation task that
addresses the problem of turning a photo into a pseudo-hand drawn sketch. This
area does have a security and police angle - on occasion, when given a poor
quality image of the face of a suspect, turning it into a sketch can help to
idetify features that may not be as prominent in the original photo.
Especially when machine learning is involved, allowing the ISGAN to understand
and augment the original image. ISGAN is not the only GAN model that has been
applied to this task, but, when the benchmark tests were run against the
current state-of-the-art methods, ISGAN was either on par or above them in
scoreYan.2021 .

###  6.11 InfoGAN

The Information Maximising Generative Adversarial Network, or InfoGAN, model
was first proposed in Chen et al, in 2016 Chen.2016 . The authors noted the
ability of InfoGAN’s model to untangle images of handwritten characters. The
model was tested and trained using the MNIST dataset (see Section 5.8). It was
also utilised on 3-dimensional images of faces, and on pictures of house
street numbers. In performance, the InfoGAN model adds ’negligible’ complexity
to the vanilla GAN (6.1) model. The training itself was based on the training
done for a DCGAN (6.3), rather than a vanilla GAN.

###  6.12 fvGAN

In Li202038 , the authors develop a method by which they can utilise GAN to
build malicious code into PDF files in such a way as to evade detection by
even schemes dedicated to the detection of PDF malware. The proposed method,
feature vector GAN or fvGAN, took in the fact that features in PDF files are
highly interconnected and interdependent, meaning one cannot simply change the
features to the ones required. First they had to pull out the features that
were most essential. Using mimicus, an invention of their own design, they
were able to pull feature vectors with 135-dimensions from the files. Once the
fvGAN had been trained on the Contagio and Surrogate datasets (from the
original PDFRate studylaskov2014practical ) of both malicious and benign PDFs,
they used it to create PDFs with content injection attacks to great effect.

###  6.13 CycleGAN

The CycleGAN model is foremost an image translation mechanism Zhu.2019 .
Cycling an ’unpaired’ image from one domain to another is its main purpose.
Proposed in 2017 by Zhu et al Zhu.2017 , CycleGANs have become a reliable tool
in image processing, and have assisted researchers in many domains. With
regards to security, it is important to note that CycleGAN models can be used
for biometrics - particularly facial recognition. More recently, a CycleGAN
variant was proposed for video-to-video translations - Mocycle-GAN
Amsaleg.2019 . This raises the possibility of using this type of GAN to build
facial recognition into CCTV software.

###  6.14 ProGAN

The Proximity Generative Adversarial Network, or ProGAN gao2019progan , is
meant to preserve the proximities of instances and samples that are reduced in
dimensionality. The original proximity in the space prior to dimensionality
reduction must be preserved, and thus ProGan was created. The proximity of
nodes in this subject is classed as first-order, second-order, and so on. If a
node is connected to another node with an edge, it is considered first-order.
These relationships are preserved through network embedding.

###  6.15 MGAN

Mixture Generative Adversarial Networks (MGAN) was proposed focusing on
overcoming the mode collapse problem in vanilla GAN models (see Section
6.1.1). This problem is a serious risk for standard GAN models. MGAN seeks to
address that issue by using multiple generators to create generated output
based on the real data given to the discriminator hoang2018mgan . The
generators are trained simultaneously, rather than sequentially, and the
resulting distributions can be mixed to achieve a realistic distribution. The
ultimate goal is to create a three-party minmax game, rather than the
traditional two party game with vanilla GAN. The parties involved in a MGAN
minmax game are: the discriminator, the classifier, and the set of generators.
The different generators are meant to work harmoniously, and the authors point
out the importance of having the different generators specialise in different
data modes.

GAN Models | Balancing datasets | Attack & Security | Malware | Functional Malware | Adversarial Examples | Malicious Traffic | Feature Extraction | Phishing URLs | Behaviour Tracking
---|---|---|---|---|---|---|---|---|---
Goodfellow GANgoodfellow2014generative  | X | X | X | X | X |  |  |  |
CoGAN |  |  | X |  | X |  |  |  |
DCGAN | X | X | X | X | X |  |  |  |
ALI-GAN |  |  | X |  | X |  |  |  |
CoRGAN |  |  | X |  | X |  |  |  |
CoRAGAN |  |  | X |  | X |  |  |  |
Sequential GAN | X | X | X | X | X |  |  |  |
GenAtSeqGAN |  |  |  |  |  |  |  |  | X
LSTM GAN |  | X | X | X | X |  |  |  | X
Bi-objective GAN |  | X | X | X | X |  |  |  |
WGAN-GP |  |  | X | X | X |  |  | X |
MalGAN |  | X | X | X | X |  |  |  |
CGAN |  |  |  |  |  |  |  |  |
fvGAN |  | X |  |  |  |  | X |  |
TrafficGAN |  | X | X |  |  | X |  |  |
Mal-IGAN | X |  | X | X | X |  |  |  |
n-gram MalGAN |  |  |  |  |  |  |  |  |
IDSGAN | X |  |  |  | X | X |  |  |
iKnight |  |  |  |  | X | X | X |  |
PDF-GAN |  |  | X | X | X | X |  |  |
GAN-tCDGAN | X |  | X |  | X |  |  |  |
IDS-GAN |  |  |  |  |  | X |  |  |
Mal-LSGAN |  | X | X | X | X | X | X |  |

Table 3: The different types of GAN models surveyed in this paper with their
most common uses.

##  7 Areas of Use

There are many areas in which Generative Adversarial Networks are of use, and
these include a selection of cybersecurity related topics. A broad overview of
the types of use each different model of GAN is used in is shown in the table
in Section 6.15.

###  7.1 Classification and Images

One of the first, and enduring, tasks for which GANs are used is that of image
classificationzhu2018generative . The ease with which GANs can compare and
create images with the necessary similarities and contextual elements, meaning
the creation of many images that belong to clear classes, is a task that GAN
models do so well even non-image based tasks are often translated into
imagesKargaard2018411  for the ease it provides when creating augmented
datasets using GAN schemes. The implications for the us in malware are self-
evident. The ability to generate new samples of malware families in order to
train machine learning based detection schemes on what the features of a
family of malware are, is a leap forwards in terms of defensive technology.
for an example, in Moti.2019 , the authors use deep learning GAN models to
generate unseen malware examples and train schemes against the signatures of
these new malware images. While the authors did not achieve a high increase in
classification, the principle of their work has been examined by others as
well. One study, Lu.2019  found they could increase classification accuracy of
malware samples by 6% through the generation of synthetic examples of malware
for training purposes. In f2 , authors implemented GAN for the classification
of greyscale images that were created by transforming malware files with
feature extraction. This task is suited to GAN schemes because GAN, more so
than any other problem space, excels at image classification. In this study,
the classifier’s performance improved by approximately 6%. In Wang20203775 ,
the authors again translate malware files into greyscale images in order to
use GAN models on them for classification. GAN systems are excellent at
picking out images that have significant similarity, which in this case means
that they belong to the same malware families. Using the Microsoft Malware
Classification Challenge 82n , the system is run against AE-SVM
yousefi2017autoencoder , tDCGAN kim2018zero , Strand drew2016polymorphic , and
MCSC-asm ni2018malware , It performs better when classifying the malware
family the images belong to than these state of the art classifiers, with the
lowest error rate.

###  7.2 Data Augmentation, Rare Classes, and Balancing a Dataset

Machine learning models suffer from a desperate need for training data. The
amount of data needed to train complex systems to the point at which they know
how to deal with the data coming into their systems is enormous. And, unless
they are an unsupervised learning model, that data requires logging and
labelling. This task is an enormous undertaking, and one that requires human
input, hours upon hours of sitting at computers and labelling each piece of
data that will be sent to the model for training and verification processes.
GAN is an effective tool to potentially solve some of the issues which arise
out of a need for data augmentationMimura2020 . Instead of an individual
creating hashes of new malware as it arrives, GANs can be used to generate and
generalise synthetic malware examples for machine learning models. Rather than
learning through the hashed values of malware files, the GAN can produce
images of malware files and rare families for training purposes. The
importance of feature extraction for learning in malware detection is of
particular weight in this scenario. This is shown clearly in Lu.2019 , in
which they augment their malware datasets with synthetic samples created by
GAN models. There are other problems with the data necessary for machine
learning models too.
Augmenting an existing dataset such that the different types of data, the
different classes, are able to be trained for the highest levels of accuracy
is highly important in classifying data. It is a major use of GAN schemes (see
mariani2018bagan ; Tan.2020 ; Zhang2022900 ; Wang2021 ;
10.32604/cmc.2022.029858 ; Guo2021 ; undefined ; Alghazzawi20223877 ). Taking
a dataset that is perhaps too small, or has classes that are too imbalanced to
learn robustly, is an area GAN models shine. As an example, Moti.2020  focuses
on using GAN models to create new samples of different classes of malware in
order to balance a dataset on which to train machine learning models.
In many existing datasets, especially those related to cybersecurity, there
exists a significant imbalance in the classes of data within a dataset. There
are many sets where the class ratios are significantly imbalanced. As seen in
Chen2021 , a dataset with very unbalanced classes can be made more even across
types using a GAN to solve the rarity of certain classes. The involved
datasets were malware designed as Android APKs, which the authors translated
into greyscale images. By supplementing these datasets of Android malware with
GAN methods, the authors were able to achieve increases of 5-20% in the
F1-score. This shows the power of GAN models in augmenting rare data.

###  7.3 Zero Day Malware

The accurate and speedy classification of malware files and malicious code in
computer systems is a expansive task, and one that has remained an enduring
problem in the cybersecurity domainkim2018zero . This is a task that is
getting bigger and more pressing, not losing significance. The ease with which
malicious code can be edited once antivirus software has been updated to
detect that particular type of malware means that the creators of this malware
have the edge in the battle between attackers and defenders. The sophisticated
attacks using polymorphic malware (see 8.2, which morphs and changes itself in
order to escape detection, make the task of identifying the malicious code
even more difficultdrew2016polymorphic . Traditional antivirus software relies
on the use of hash codes. Once the authors of the antivirus software have
identified a piece of malicious code, it is hashed into a value string that
specifically identifies that piece of code. The antivirus definitions are
updated, and the program knows how to recognize that code, should it come into
contact with the computer on which the defense software is installed. This
does, however, rely on the idea that this malware has been identified before
it turns up on the target system. Because the slightest change in the
malicious code causes cascading changes in the hash code, all a malware
developer needs to do to escape the antivirus system is move a portion of code
to a different place within the program. This retains functionality, while
changing the hash code of the program enough that it will no longer be picked
up by traditional antivirus schemes. Until the antivirus developers find and
identify the new variant of the malware, the traditional scheme will not
notice the altered malware and it will be able to operate undetected. GAN has
become a tool for building new adversarial examples that the defender can
train with to prevent this outcomeKang20214105 . Similarly, in
10.1109/access.2021.3056482 , the authors address this problem using GAN-based
adversarial examples to train their blockchain method of intrusion detection.
Their LSTM-CGAN model for generating these examples allowed the resulting
classifiers to jump several percentage points in accuracy when the classifier
was trained on the AEs as opposed to when it was trained on the unenhanced
dataset.
One of the bigger stumbling blocks in malware detection is the unseen or
‘zero-day’ attacksbilge2012before . Because of the types of data that a GAN
model can create, generating new adversarial examples and zero day malware is
a possible way to train a security model on unseen data. In Liu2020 , the
authors use a GAN model they name TrafficGAN to create new malicious traffic
patterns for zero-day attacks, partially by including noise into the data as
substitution for some unseen traffic, In Moti2019319 , a standard variation
GAN with a LeakyReLU activation function is implemented to train IDS models on
unseen malware. While their contribution on the whole is an increase of only
1% accuracy overall, the idea of using the data generated by GAN to increase
the robust nature of a model on data it hasn’t seen in training sets is a
useful and practical one. In fact, the authors of Hu.2017  use GAN modelling
to create black-box attack methods. Their chosen model in particular, MalGAN
(see Section 6.7), was created entirely towards the view that GAN schemes
could create functional and unseen examples of malware to attack machine
learning systems. The attacks they conducted on non-neural network systems
managed to achieve a True Positive Rate of zero, while on the neural network
based models (RF, LR, DT, SVM, MLP, VOTE), which had achieved a TPR of 92%
minimum prior to the attack tests, managed to achieve a TPR of 0.19% maximum
when the generated attack data was integrated into the testing set. This study
shows how vulnerable IDS or malware detection systems are to the data and
attacks generated by a malicious GAN. On the other had, when the authors of
Umer.2021  generated their own zero-day attacks and added them into the
training datasets for their IDS, they were able to achieve a success rate of
84% when classifying unseen malware. Utilising the Microsoft Malware
Classification Challenge (2015) dataset 82n , they outperformed 14 other
state-of-the-art systems, and achieved a 98% success rate when classifying the
zero day attacks. This shows that using GAN to create unseen examples and
zero-day attacks can be a powerful force for creating truly robust classifiers
and detectors.

###  7.4 Detection Evasion

Given the success at generating unseen examples in order to train more robust
systems, the use of GAN methods to create malicious code which evades
detection is a logical step. Building GAN schemes which can generate malicious
files that evade current detection schemes is necessary for the training of
the next generation of malware detection schemes. In Zhu.2021o7q , the authors
create a scheme using GAN to evade detection from a broad range of machine
learning models - Multi-layer Perceptron, Decision Tree, Logistical
Regression, Support Vector Machine, Random Forest - and found that feature
extraction was the key in making their scheme achieve the minimal True
Positive Rate (TPR). As they increased the selection of features in both
attacker and defender, the TPR rose. While the authors take the steps of
adding benign features into the malicious examples they create, they achieve a
slightly less impressive TPR than the previously mentioned scheme - the lowest
TPR is under 11%. Unlike the previous model, the authors utilise n-gram
feature selection, a method borrowed primarily from Natural Language Schemes,
but which removes the step of translating the malware into a different form -
such as the popular method of making malware ’images’ - and allows the model
to work on the raw data itself. The creation of the MalFox GAN scheme in
Zhong.2020  shows the ability of GAN schemes to create black-box attacks, in
which the attacker knows nothing about the actual structure of the system they
are attacking. In a black box attack, the attack must be generalised enough
that it can be employed successfully against any defender. In MalFox, which
was trained specifically with Obfusmal, Stealmal, and Hollowmal as techniques
to perturb the data for attacking, when used against the online malware
repository Virus Share 9vs , the detection rate was minimised to 56.2%. In
Li.2021 , the authors use a standard GAN model against a Deep Neural Network
defence system in order to create Windows malware that is perturbed just
enough to evade detection and retain its original purpose and function. The
authors achieved this using raw byte sequences and training the GAN on
existing byte sequences of malware. They achieved evasion rates of more than
50%.

###  7.5 Applied Attack GAN Models

The flipside of using GAN to generate unseen malware and use it to train
antivirus and IDS models, is the use of GAN to create highly effective,
theoretical attacks on existing systems. The possibilities offered by GANs for
creating new ‘adversarial examples’ to use against detection systems, mean
that there is significant opportunity to build new malware with the same
functionality, but in a way that is fast and avoids detection with high rates
of success. As we discussed in Section 6, the creation of models like MalGAN
and MalFox (see Section 6.7 and 6.6), are clear demonstrations of the attack
potential of GAN models in generating malware. In Li.2018p6b , the authors
managed to build a model which evaded firewalls in order to attack Android
systems with a success rate of 95%. This is a troubling note for security
against new malware creation techniques. The use of these systems to train the
new generation of malware detection schemes should be therefore a high
priority for security researchers and developers.

##  8 Discussion

As popular as GAN research is, there are almost as many types of proposed
models for GAN as there are papers discussing GANs. There are some which can
be easily seen and categorised as types of the same genus, and then there are
those rare new examples, which offer a fresh method for implementing GANs
within a research or work setting. Surveying as many papers proposing
solutions to difficulties in particular sectors results in surveying almost as
many problems in others. We have gathered some particular points of interest
and problems which may offer future lines of research or simply act to temper
future models with an eye towards creation of new malware detection schemes.

###  8.1 Malware and the Sandbox

One issue with identifying malware based on features and contextual
relationships is that it may require the malware be run before it can be
accurately identified. This type of analysis is dynamic analysis, whereas
detection systems which use the file without running it are performing static
analysis. The majority of the papers we have assessed in this study propose
static analysis Zolkipli.2011 . However, this then causes concern for the
implementation of these methods - can they run successfully in real-time,
stopping the malware from running even before it has been classified? This
puts a level of vulnerability into the scheme. What damage will be done while
the machine identifies the currently executing program as malware? A
mitigation for this type of problem is to run the programs in a sandbox before
classification. This means they cannot affect the performance of the system or
exploit any vulnerabilities prior to identificationlindorfer2011detecting .
Another suggestion is that the program is not run until the binaries have been
extracted and used to try to identify the malware family to which it belongs.
This problem is not specific to Deep Learning or Machine Learning based
detectors - it has been of concern for many years, and has often been
addressed using the sandbox option. However, this simply lead to the creation
of "environmental detection" in malwareyokoyama2016sandprint , allowing the
code to detect when it was being run in a sandbox and when it was in the real
system environment.

###  8.2 Polymorphism, Evolution and the Dangers of Malware

Malware developers have taken stock of the current state of research in the
area and used it to their advantage. New types of polymorphic malware, which
twists and turns itself into code-based pretzels in order to avoid detection,
are able to fool signature-based detection systems Zolkipli.2011 . GANs
schemes have the ability to perturb the code of existing malware code and use
these unseen examples to train new models. This ability has potential to
defend against polymorphic malware. One method, discussed in Zolkipli.2011 ,
is to use malware behaviour to teach new detection systems. Of course, this
has innate risk, because the malware has to be executed in order to complete
the behaviour analysis. In such a case, the idea would be to run the malware
sample in a ’sandbox’, as discussed in Section 8.1. Another potential problem
is fighting the method by which the malware is spread. Social engineering
hacking, sending malicious files to email, or using watering hole
attacksiiiiiiiiiA watering hole attack involves infecting a site the target is
likely to visit, rather than directly attacking the target’s device. See nq7
for a detailed examination of waterholing attacks. to download the malware
onto the target computer Gazet.2010  are all tasks that are reliant on the
ways in which the user interacts with their devices. Ideally, a new and
improved machine learning based detection scheme would protect users even when
they clicked on email links or went to download a PDF from a new site. This
makes training effective and accurate classifiers are priority in
cybersecurity, and one of the preeminent areas of malware research.

###  8.3 Optimisation and Nature Inpired Computing

In all machine learning models based on Neural Networks, there is a need to
optimise the model. The number of layers, the nodes in each layer, the
activation function, the weights. All of these make the model significantly
better or worse at its task, and for the most part, they are altered and
improved through trial and error. One group of researchers, however, has taken
this optimisation problem and made it the focus of their research into GAN
models Du2020 . Using Genetic Algorithms, they run through the different
options for optimisation automatically, with the non-denominated sorting
genetic algorithm (NSGA-II) finding the best values for the GAN model. This
true positive score of the optimised GAN was more than 98% on the MNIST
dataset, introduced in lecun1998gradient , which is made up of images of
handwritten letters. The same format for optimisation was employed when the
model ran on the malware dataset taken from the Vision Institute
nataraj2011malware , and achieved a true positive rate of 97.87%, a score
several points higher than the GAN run on the same dataset without the
optimisation of the NSGA-II algorithm. This suggests there is a road to take
for optimising the layout and structure of each of the GAN models, and that it
is possible another Nature-Inspired Computing area may be how that is
achieved. As such, it is an interesting direction for malware researchers to
explore.

###  8.4 Data Format and Translation

In many of the examples profiled in this paper, the data, be it traffic flow,
machine language, API calls, or malware files, is often translated into
different types of images. This makes the job of the GAN model easier because
they work so well with image classification tasks, but it also means that
there is additional complexity in the algorithms due to the necessity of
translating the data into the correct format. There are papers, however, such
as Moti.2020 , Sur2020 , Hu.2017 , Zhong.2020 , Yumlembam.2022 , or Li202038
which use the raw data, without translating it to a type more palatable to a
GAN scheme. This is important because of the overhead of these different
models. When investigating to find an efficient model for use in a given
domain, ensuring that there is little to no additional or unnecessary overhead
seems a significant consideration.
One thing common amongst almost all papers discussed is the need to change the
form in which the data is used. The pre-processing, referred to in
Alavizadeh.2022  as data triage, is a cost-expensive and high overhead
requirement in both time and processing power. There are those that have
managed to avoid translating the data overmuch - such as the research
presented in Liu2020  \- but most translate data into images or carefully
sectioned bytes of data. This is an area of concern while real-time
application of a GAN based IDS or firewall is a goal. To further the idea of a
real-time GAN scheme, the methods of data pre-processsing need to be carefully
examined. They offer unacceptably large overheads for real world application.
Because malware files are generally simple and popular to translate into
images, the task is not as arduous as it might be in other areas of interest.

###  8.5 Ethics and Responsibility in Malware Research

The ethical questions posed in mirjalili2018semi  offer an interesting path
for future research - there is a general lack of discussion in GAN research
papers about the ramifications of possible misappropriation or
misclassification of the work contained within. This is an important step to
consider in all academic research, and the ethical implications of things like
Deepfakewesterlund2019emergence , or AdvandMalWang2021Mi  are likely to cause
extreme and often unintended effects. Whether by GAN or VAE, the type of
research in papers which create new models for malware synthesis are an
excellent example of why researchers need to be careful in balancing the quest
for knowledge and the security/ethical risks of their research. Knowing this
type of attack is possible, and with the results they achieved, is important
for those who develop defensive mechanisms for this type of attack, but it is
also useful to black hat individuals and malicious operators. The amount of
detail over the model created, how it operates, and was trained is where that
balance of ethics and information comes into play. These things must be looked
at carefully and applied with consideration. The situation of neural network
applications as they are demands that we take stock of what has been built,
how the dataset has been labelled, how the features have been established, and
how/where the neural network is going to be deployed. While this work is
starting to take place in the field - see matthias2004responsibility
,sand2022responsibility  \- there is much still to cover, and researchers like
those in martin2019designing  are offering frameworks for incorporating ethics
into the fabric of machine learning research. Therefore, like many research
topics, it has a flipside, and both sides need serious ethical review and
consideration to ensure that the research benefits those who are most
vulnerable.

##  9 Future Research Directions

There are still many avenues for potential research. The methods employed by
the authors in Zhu.2021o7q  to avoid the popular step of translating the
dataset into sequences or images and instead working on the data directly
using the n-gram feature extraction method is certainly an area worthy of
future research for more applications. The incredibly low detection rate
achieved by The use of Genetic Algorithms to optimise the performance metrics
of GAN models Du2020  is an avenue which could prove very fruitful. The
development of real-time, dynamic analysis and detection is a challenge
researchers are still only beginning to scratch the surface of Zolkipli.2011 ,
and requires further research into the types of secure environments in which
this analysis can safely take place. The realm of malware research contains so
many possible avenues for research when it comes to GAN algorithms, and this
has been illustrated in this paper to the best of our ability.

##  10 Conclusion

This paper has presented a wide range of research in the current malware
research space, using different types of Generative Adversarial Models. Our
aim is to have provided an explanation of not only what Generative Adversarial
Networks are and how they are trained and assessed, that we have also given an
effective grounding in the applications within the malware research community,
which GANs may work with both in the current literature and in any potential
future research. To that end, we have iterated through the explanation of
GAN’s basic functions; the work on other surveys done in related areas,
particularly to demonstrate that there has yet to be an in-depth survey paper
on the uses of GAN in cubersecurity and malware; we have explained the
different metrics used for evaluation; we gave an in-depth review of the
datasets currently favoured in the problem space; we included a list on the
different GAN models that are discussed throughout the survey; we have delved
in depth to the areas of use that are currently most popular, we have
endeavoured to provide a discussion and survey that goes into detail so as to
give the reader the full picture; and we have presented potential future
avenues for research in the area. We hope that this survey of malware research
through the lens of Generative Adversarial Networks, and the way in which they
can be employed, has given the reader an idea of where to start with their own
research in this area, or given them an update state of the art. The field of
GANs for malware research is only getting started, and there is much to do,
and many questions to be answered.

## Declarations

### Competing interests

The authors declare that they have no competing interests.

### Funding

The authors would like to thank the Ministry of Business, Innovation, and
Employment (MBIE) from the New Zealand Government to support our work with the
grant (MAUX1912) which made it possible for us to conduct the research.

## References

  * (1) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Nets. Advances in Neural Information Processing Systems 27 (2014)
  * (2) Jang, S., Li, S., Sung, Y.: Generative adversarial network for global image-based local image to improve Malware classification using convolutional neural network. Applied Sciences (Switzerland) 10(21), 1–14 (2020). doi:10.3390/app10217585
  * (3) Pham, T.D., Pham, T.T.T., Hoang, S.T., Ta, V.C.: Exploring Efficiency of GAN-based Generated URLs for Phishing URL Detection. 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) 00, 1–6 (2021). doi:10.1109/mapr53640.2021.9585287
  * (4) Aggarwal, A., Mittal, M., Battineni, G.: Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights 1(1), 100004 (2021). doi:10.1016/j.jjimei.2020.100004
  * (5) Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., Wang, F.-Y.: Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica 4(4), 588–598 (2017)
  * (6) Amin, M., Shah, B., Sharif, A., Ali, T., Kim, K.-I., Anwar, S.: Android malware detection through generative adversarial networks. Transactions on Emerging Telecommunications Technologies 33(2) (2022). doi:10.1002/ett.3675
  * (7) O’Kane, P., Sezer, S., Carlin, D.: Evolution of ransomware. IET Networks 7(5), 321–327 (2018). doi:10.1049/iet-net.2017.0207
  * (8) Tang, W., Tan, S., Li, B., Huang, J.: Automatic Steganographic Distortion Learning Using a Generative Adversarial Network. IEEE Signal Processing Letters 24(10), 1547–1551 (2017). doi:10.1109/lsp.2017.2745572
  * (9) Corley, I., Lwowski, J., Hoffman, J.: Destruction of Image Steganography using Generative Adversarial Networks. arXiv (2019). 1912.10070
  * (10) Kumar, A., Biswas, A., Sanyal, S.: eCommerceGAN : A Generative Adversarial Network for E-commerce. arXiv (2018). 1801.03244
  * (11) Ebrahimi, M., Chai, Y., Satani, S., Chen, H.: Cross lingual cybersecurity analytics in the international dark web with adversarial deep representation learning. MIS Quarterly Forthcoming (2021)
  * (12) Berman, D.S., Buczak, A.L., Chavis, J.S., Corbett, C.L.: A survey of deep learning methods for cyber security. Information (Switzerland) 10(4), 122 (2019). doi:10.3390/info10040122
  * (13) Liu, J., Nogueira, M., Fernandes, J., Kantarci, B.: Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems. IEEE Communications Surveys and Tutorials 24(1), 123–159 (2022). doi:10.1109/comst.2021.3136132
  * (14) Navidan, H., Moshiri, P.F., Nabati, M., Shahbazian, R., Ghorashi, S.A., Shah-Mansouri, V., Windridge, D.: Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks 194, 108149 (2021). doi:10.1016/j.comnet.2021.108149. 2105.04184
  * (15) Li, J., Zhou, L., Li, H., Yan, L., Zhu, H.: Dynamic traffic feature camouflaging via generative adversarial networks. In: 2019 IEEE Conference on Communications and Network Security (CNS), pp. 268–276 (2019). IEEE
  * (16) Arora, A., Shantanu: A Review on Application of GANs in Cybersecurity Domain. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) 39(2), 433–441 (2022). doi:10.1080/02564602.2020.1854058
  * (17) Eberhart, R.C., Dobbins, R.W., Hutton, L.V.: Performance metrics. In: Neural Network PC Tools, pp. 161–176. Academic Press, ??? (1990). doi:10.1016/B978-0-12-228640-7.50013-1
  * (18) Barratt, S., Sharma, R.: A Note on the Inception Score. arXiv (2018). doi:10.48550/arxiv.1801.01973. 1801.01973
  * (19) Salehi, P., Chalechale, A., Taghizadeh, M.: Generative adversarial networks (gans): An overview of theoretical model, evaluation metrics, and recent developments. arXiv preprint arXiv:2005.13178 (2020)
  * (20) Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
  * (21) Borji, A.: Pros and Cons of GAN Evaluation Measures. arXiv (2018). doi:10.48550/arxiv.1802.03446. 1802.03446
  * (22) Plohmann, D.: DGArchive A deep dive into domain generating malware (2015). https://www.botconf.eu/wp-content/uploads/2015/12/OK-P06-Plohmann-DGArchive.pdf Accessed 2022-08-26
  * (23) Choudhary, C., Sivaguru, R., Pereira, M., Yu, B., Nascimento, A.C., Cock, M.D.: Security in Computing and Communications, 6th International Symposium, SSCC 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers. Communications in Computer and Information Science, 640–655 (2019). doi:10.1007/978-981-13-5826-5_50
  * (24) Almashhadani, A.O., Kaiiali, M., Carlin, D., Sezer, S.: MaldomDetector: A system for detecting algorithmically generated domain names with machine learning. Computers & Security 93, 101787 (2020). doi:10.1016/j.cose.2020.101787
  * (25) VirusTotal - Home. https://www.virustotal.com/gui/home/upload Accessed 2022-08-26
  * (26) Anderson, H.S., Roth, P.: EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models. arXiv (2018). 1804.04637
  * (27) Zhao, H., Li, M., Wu, T., Yang, F.: Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection. International Journal of Computational Intelligence Systems 11(1), 1153–1169 (2018). doi:10.2991/ijcis.11.1.87
  * (28) Canbek, G., Sagiroglu, S., Temizel, T.T.: New Techniques in Profiling Big Datasets for Machine Learning with a Concise Review of Android Mobile Malware Datasets. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) 00, 117–121 (2018). doi:10.1109/ibigdelft.2018.8625275
  * (29) Singh, G., Khare, N.: A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques. International Journal of Computers and Applications 44(7), 1–11 (2021). doi:10.1080/1206212x.2021.1885150
  * (30) Parkour, M.: Contagio Malware Dataset (2020). http://contagiodump.blogspot.com/ Accessed 2022-08-26
  * (31) Sharma, S., Kumar, N., Kumar, R., Krishna, C.R.: The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach. Communications of the Association for Information Systems 46, 619–637 (2020). doi:10.17705/1cais.04626
  * (32) Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., Rieck, K.: Drebin: Effective and Explainable Detection of Android Malware in Your Pocket. Proceedings 2014 Network and Distributed System Security Symposium (2014). doi:10.14722/ndss.2014.23247
  * (33) Comodo Anti Malware Database Latest Version & Additions 2022. https://www.comodo.com/home/internet-security/updates/vdp/database.php Accessed 2022-08-26
  * (34) Comodemia | Get Your Academics to Collaborate with Comodo. https://comodemia.comodo.com/ Accessed 2022-08-26
  * (35) Ye, Y., Chen, L., Hou, S., Hardy, W., Li, X.: DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowledge and Information Systems 54(2), 265–285 (2018). doi:10.1007/s10115-017-1058-9
  * (36) Forensics, C.: VirusShare - Because Sharing is Caring. www.virusshare.com
  * (37) Düzgün, B., Çayır, A., Demirkıran, F., Kahya, C.N., Gençaydın, B., Dağ, H.: Benchmark Static API Call Datasets for Malware Family Classification. arXiv (2021). 2111.15205
  * (38) Menéndez, H.D., Bhattacharya, S., Clark, D., Barr, E.T.: The arms race: Adversarial search defeats entropy used to detect malware. Expert Systems with Applications 118, 246–260 (2019). doi:10.1016/j.eswa.2018.10.011
  * (39) Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft Malware Classification Challenge. arXiv (2018). doi:10.48550/ARXIV.1802.10135. https://arxiv.org/abs/1802.10135
  * (40) Chivukula, R., Vamsi, M., Jaya, T., Harini, M.: Empirical Study on Microsoft Malware Classification. International Journal of Advanced Computer Science and Applications 12(3) (2021). doi:10.14569/ijacsa.2021.0120361
  * (41) Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft Malware Classification Challenge. arXiv (2018). 1802.10135
  * (42) Narayanan, B.N., Djaneye-Boundjou, O., Kebede, T.M.: Performance analysis of machine learning and pattern recognition algorithms for malware classification. In: 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), pp. 338–342 (2016). doi:10.1109/NAECON.2016.7856826
  * (43) Kebede, T.M., Djaneye-Boundjou, O., Narayanan, B.N., Ralescu, A., Kapp, D.: Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset. 2017 IEEE National Aerospace and Electronics Conference (NAECON), 70–75 (2017). doi:10.1109/naecon.2017.8268747
  * (44) Kalash, M., Rochan, M., Mohammed, N., Bruce, N.D.B., Wang, Y., Iqbal, F.: Malware Classification with Deep Convolutional Neural Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), 1–5 (2018). doi:10.1109/ntms.2018.8328749
  * (45) Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998). doi:10.1109/5.726791
  * (46) Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv (2016). 1606.03657
  * (47) Cheng, K., Tahir, R., Eric, L.K., Li, M.: An analysis of generative adversarial networks and variants for image synthesis on MNIST dataset. Multimedia Tools and Applications 79(19-20), 13725–13752 (2020). doi:10.1007/s11042-019-08600-2
  * (48) Cohen, G., Afshar, S., Tapson, J., Schaik, A.v.: EMNIST: Extending MNIST to Handwritten Letters. 2017 International Joint Conference on Neural Networks (IJCNN), 2921–2926 (2017). doi:10.1109/ijcnn.2017.7966217
  * (49) Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv (2014). 1411.1784
  * (50) Durall, R., Chatzimichailidis, A., Labus, P., Keuper, J.: Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues. arXiv (2020). 2012.09673
  * (51) Thanh-Tung, H., Tran, T.: Catastrophic forgetting and mode collapse in GANs. 2020 International Joint Conference on Neural Networks (IJCNN) 00, 1–10 (2020). doi:10.1109/ijcnn48605.2020.9207181
  * (52) Gauthier, J.: Conditional generative adversarial nets for convolutional face generation. Class project for Stanford CS231N: convolutional neural networks for visual recognition, Winter semester 2014(5), 2 (2014)
  * (53) Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  * (54) Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806 (2014)
  * (55) Mordvintsev, A., Olah, C., Tyka, M.: Inceptionism: Going deeper into neural networks (2015)
  * (56) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015). PMLR
  * (57) Mustapha, I.B., Hasan, S., Nabus, H., Shamsuddin, S.M.: Conditional Deep Convolutional Generative Adversarial Networks for Isolated Handwritten Arabic Character Generation. Arabian Journal for Science and Engineering 47(2), 1309–1320 (2022). doi:10.1007/s13369-021-05796-0
  * (58) Abdleazeem, S., El-Sherif, E.: Arabic handwritten digit recognition. International Journal of Document Analysis and Recognition (IJDAR) 11(3), 127–141 (2008). doi:10.1007/s10032-008-0073-5
  * (59) Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: 5th International Conference on Learning Representations. International Conference on Learning Representations (2017)
  * (60) Yang, Q., Li, X.: BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network. BMC Bioinformatics 22(1), 357 (2021). doi:10.1186/s12859-021-04273-7
  * (61) Zhong, F., Cheng, X., Yu, D., Gong, B., Song, S., Yu, J.: MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors. arXiv (2020). 2011.01509
  * (62) Hu, W., Tan, Y.: Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN. arXiv (2017). 1702.05983
  * (63) Wang, J., Chang, X., Wang, Y., Rodríguez, R.J., Zhang, J.: LSGAN-AT: enhancing malware detector robustness against adversarial examples. Cybersecurity 4(1), 38 (2021). doi:10.1186/s42400-021-00102-9
  * (64) Wang, J., Chang, X., Mišić, J., Mišić, V.B., Wang, Y., Zhang, J.: Mal-LSGAN: An Effective Adversarial Malware Example Generation Model. 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings 00, 1–6 (2021). doi:10.1109/globecom46510.2021.9685442
  * (65) Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Smolley, S.P.: Least Squares Generative Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV), 2813–2821 (2017). doi:10.1109/iccv.2017.304
  * (66) Odena, A., Olah, C., Shlens, J.: Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv (2016). 1610.09585
  * (67) Yan, L., Zheng, W., Gou, C., Wang, F.-Y.: IsGAN: Identity-sensitive generative adversarial network for face photo-sketch synthesis. Pattern Recognition 119, 108077 (2021). doi:10.1016/j.patcog.2021.108077
  * (68) Li, Y., Wang, Y., Wang, Y., Ke, L., Tan, Y.-A.: A feature-vector generative adversarial network for evading PDF malware classifiers. Information Sciences 523, 38–48 (2020). doi:10.1016/j.ins.2020.02.075
  * (69) Laskov, P., et al.: Practical evasion of a learning-based classifier: A case study. In: 2014 IEEE Symposium on Security and Privacy, pp. 197–211
  * (70) Zhu, M.M., Gong, S., Qian, Z., Zhang, L.: A Brief Review on Cycle Generative Adversarial Networks. Proceedings of The 7th International Conference on Intelligent Systems and Image Processing 2019, 235–242 (2019). doi:10.12792/icisip2019.046
  * (71) Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV), 2242–2251 (2017). doi:10.1109/iccv.2017.244
  * (72) Amsaleg, L., Huet, B., Larson, M., Gravier, G., Hung, H., Ngo, C.-W., Ooi, W.T., Chen, Y., Pan, Y., Yao, T., Tian, X., Mei, T.: Mocycle-GAN. Proceedings of the 27th ACM International Conference on Multimedia, 647–655 (2019). doi:10.1145/3343031.3350937
  * (73) Gao, H., Pei, J., Huang, H.: Progan: Network embedding via proximity generative adversarial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1308–1316 (2019)
  * (74) Hoang, Q., Nguyen, T.D., Le, T., Phung, D.: MGAN: Training generative adversarial nets with multiple generators. International Conference on Learning Representations (2018)
  * (75) Zhu, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Generative adversarial networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 56(9), 5046–5063 (2018)
  * (76) Kargaard, J., Drange, T., Kor, A.-L., Twafik, H., Butterfield, E.: Defending IT systems against intelligent malware. Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, 411–417 (2018). doi:10.1109/dessert.2018.8409169
  * (77) Moti, Z., Hashemi, S., Namavar, A.: Discovering Future Malware Variants By Generating New Malware Samples Using Generative Adversarial Network. 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) 00, 319–324 (2019). doi:10.1109/iccke48569.2019.8964913
  * (78) Lu, Y., Li, J.: Generative Adversarial Network for Improving Deep Learning Based Malware Classification. 2019 Winter Simulation Conference (WSC) 00, 584–593 (2019). doi:10.1109/wsc40007.2019.9004932
  * (79) Lu, Y., Li, J.: Generative Adversarial Network for Improving Deep Learning Based Malware Classification. Proceedings - Winter Simulation Conference 2019-December, 584–593 (2019). doi:10.1109/wsc40007.2019.9004932
  * (80) Wang, S., Wang, Q., Jiang, Z., Wang, X., Jing, R.: A weak coupling of semi-supervised learning with generative adversarial networks for malware classification. Proceedings - International Conference on Pattern Recognition 00, 3775–3782 (2020). doi:10.1109/icpr48806.2021.9412832
  * (81) Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft Malware Classification Challenge (BIG 2015) (2018). https://www.kaggle.com/c/malware-classification Accessed 2022-7-23
  * (82) Yousefi-Azar, M., Varadharajan, V., Hamey, L., Tupakula, U.: Autoencoder-based feature learning for cyber security applications. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3854–3861
  * (83) Kim, J.-Y., Bu, S.-J., Cho, S.-B.: Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Information Sciences 460, 83–102 (2018). doi:10.1016/j.ins.2018.04.092
  * (84) Drew, J., Moore, T., Hahsler, M.: Polymorphic malware detection using sequence classification methods. In: 2016 IEEE Security and Privacy Workshops (SPW), pp. 81–87
  * (85) Ni, S., Qian, Q., Zhang, R.: Malware identification using visualization images and deep learning. Computers & Security 77, 871–885 (2018)
  * (86) Mimura, M.: Using fake text vectors to improve the sensitivity of minority class for macro malware detection. Journal of Information Security and Applications 54, 102600 (2020). doi:10.1016/j.jisa.2020.102600
  * (87) Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: Bagan: Data augmentation with balancing gan. arXiv preprint arXiv:1803.09655 (2018)
  * (88) Tan, W.L., Truong-Huu, T.: Enhancing Robustness of Malware Detection using Synthetically-adversarial Samples. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 00, 1–6 (2020). doi:10.1109/globecom42002.2020.9322377
  * (89) Zhang, X., Wang, J., Zhu, S.: Dual Generative Adversarial Networks Based Unknown Encryption Ransomware Attack Detection. IEEE Access 10, 900–913 (2022). doi:10.1109/access.2021.3128024
  * (90) Wang, C., Zhang, L., Zhao, K., Ding, X., Wang, X.: Advandmal: Adversarial training for android malware detection and family classification. Symmetry 13(6), 1081 (2021). doi:10.3390/sym13061081
  * (91) Haroon, M.S., Ali, H.M.: Adversarial Training Against Adversarial Attacks for Machine Learning-Based Intrusion Detection Systems. Computers, Materials & Continua 73(2), 3513–3527 (2022). doi:10.32604/cmc.2022.029858
  * (92) Guo, J., Guo, S., Ma, S., Sun, Y., Xu, Y.: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-Set Scenario. IEEE Transactions on Neural Networks and Learning Systems PP(99), 1–15 (2021). doi:10.1109/tnnls.2021.3099122
  * (93) Randhawa, R.H., Aslam, N., Alauthman, M., Rafiq, H.: EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes. arXiv (2021). 2109.08026
  * (94) Alghazzawi, D.M., Hasan, S.H., Bhatia, S.: Optimized Generative Adversarial Networks for Adversarial Sample Generation. Computers, Materials and Continua 72(2), 3877–3897 (2022). doi:10.32604/cmc.2022.024613
  * (95) Moti, Z., Hashemi, S., Jahromi, A.N.: A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) 00, 48–53 (2020). doi:10.1109/iscisc51277.2020.9261913
  * (96) Chen, Y.-M., Yang, C.-H., Chen, G.-C.: Using Generative Adversarial Networks for Data Augmentation in Android Malware Detection. 2021 IEEE Conference on Dependable and Secure Computing, DSC 2021 00, 1–8 (2021). doi:10.1109/dsc49826.2021.9346277
  * (97) Kang, M., Kim, H., Lee, S., Han, S.: Resilience against Adversarial Examples: Data-Augmentation Exploiting Generative Adversarial Networks. KSII Transactions on Internet and Information Systems 15(11), 4105–4121 (2021). doi:10.3837/tiis.2021.11.013
  * (98) Liu, Z., Yin, X.: LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models. IEEE Access 9, 22616–22625 (2021). doi:10.1109/access.2021.3056482
  * (99) Bilge, L., Dumitraş, T.: Before we knew it: an empirical study of zero-day attacks in the real world. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 833–844 (2012)
  * (100) Liu, Z., Li, S., Zhang, Y., Yun, X., Cheng, Z.: Efficient Malware Originated Traffic Classification by Using Generative Adversarial Networks. Proceedings - IEEE Symposium on Computers and Communications 2020-July, 1–7 (2020). doi:10.1109/iscc50000.2020.9219561
  * (101) Moti, Z., Hashemi, S., Namavar, A.: Discovering future malware variants by generating new malware samples using generative adversarial network. 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019 00, 319–324 (2019). doi:10.1109/iccke48569.2019.8964913
  * (102) Umer, M., Saleem, Y., Saleem, M., Aman, N.: A GAN Based Malware Adversaries Detection Model. 2021 15th International Conference on Open Source Systems and Technologies (ICOSST) 00, 1–9 (2021). doi:10.1109/icosst53930.2021.9683863
  * (103) Zhu, E., Zhang, J., Yan, J., Chen, K., Gao, C.: N-gram MalGAN: Evading machine learning detection via feature n-gram. Digital Communications and Networks (2021). doi:10.1016/j.dcan.2021.11.007
  * (104) Li, X., Kong, K., Xu, S., Qin, P., He, D.: Feature selection-based android malware adversarial sample generation and detection method. IET Information Security 15(6), 401–416 (2021). doi:10.1049/ise2.12030
  * (105) Li, H., Zhou, S., Yuan, W., Li, J., Leung, H.: Adversarial-Example Attacks Toward Android Malware Detection System. IEEE Systems Journal 14(1), 653–656 (2018). doi:10.1109/jsyst.2019.2906120
  * (106) Zolkipli, M.F., Jantan, A.: An Approach for Malware Behavior Identification and Classification. 2011 3rd International Conference on Computer Research and Development 1, 191–194 (2011). doi:10.1109/iccrd.2011.5764001
  * (107) Lindorfer, M., Kolbitsch, C., Milani Comparetti, P.: Detecting environment-sensitive malware. In: International Workshop on Recent Advances in Intrusion Detection, pp. 338–357 (2011). Springer
  * (108) Yokoyama, A., Ishii, K., Tanabe, R., Papa, Y., Yoshioka, K., Matsumoto, T., Kasama, T., Inoue, D., Brengel, M., Backes, M., et al.: Sandprint: Fingerprinting malware sandboxes to provide intelligence for sandbox evasion. In: International Symposium on Research in Attacks, Intrusions, and Defenses, pp. 165–187 (2016). Springer
  * (109) Krithika, N.: A STUDY ON WHA (WATERING HOLE ATTACK) – THE MOST DANGEROUS THREAT TO THE ORGANISATION. International Journal of Innovations in Scientific and Engineering Research 4(8), 196–198 (August 2017)
  * (110) Gazet, A.: Comparative analysis of various ransomware virii. Journal in Computer Virology 6(1), 77–90 (2010). doi:10.1007/s11416-008-0092-2
  * (111) Du, L., Cui, Z., Wang, L., Ma, J.: Structure tuning method on deep convolutional generative adversarial network with nondominated sorting genetic algorithm II. Concurrency and Computation: Practice and Experience 32(14) (2020). doi:10.1002/cpe.5688
  * (112) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
  * (113) Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.S.: Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security, pp. 1–7
  * (114) Sur, C.: GenAtSeq GAN with Heuristic Reforms for Knowledge Centric Network with Browsing Characteristics Learning, Individual Tracking and Malware Detection with Website2Vec. SN Computer Science 1(4), 228 (2020). doi:10.1007/s42979-020-00234-8
  * (115) Yumlembam, R., Issac, B., Jacob, S.M., Yang, L.: IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense. IEEE Internet of Things Journal PP(99), 1–1 (2022). doi:10.1109/jiot.2022.3188583
  * (116) Alavizadeh, H., Jang-Jaccard, J., Enoch, S.Y., Al-Sahaf, H., Welch, I., Camtepe, S.A., Kim, D.D.: A Survey on Cyber Situation Awareness Systems: Framework, Techniques, and Insights. ACM Computing Surveys (2022). doi:10.1145/3530809
  * (117) Mirjalili, V., Raschka, S., Namboodiri, A., Ross, A.: Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images. In: 2018 International Conference on Biometrics (ICB), pp. 82–89 (2018). IEEE
  * (118) Westerlund, M.: The emergence of deepfake technology: A review. Technology Innovation Management Review 9(11) (2019)
  * (119) Matthias, A.: The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and information technology 6(3), 175–183 (2004)
  * (120) Sand, M., Durán, J.M., Jongsma, K.R.: Responsibility beyond design: Physicians’ requirements for ethical medical ai. Bioethics 36(2), 162–169 (2022)
  * (121) Martin, K.E.: Designing ethical algorithms. MIS Quarterly Executive June (2019)

##

Appendix A: List of Abbreviations
Abbreviation Description AC-GAN Auxiliary Classifier GAN ACC Accuracy AE
Adversarial Example AE-SVM Auto-encoder Support Vector Machine ALI-GAN
Adversarially Learned Inference GAN AML Adversarial Machine Learning API
Application Programming Interface APK Android application format AUC Area
Under the Curve Bi-LSTM Bi-dierctional Long Short-Term Memory BiGAN Bi-
directional Generative Adversarial Network C&C / CC Control and Command Server
cDCGAN Conditional Deep Convolutional GAN CF Catastrophic Forgetting CGAN
Conditional Generative Adversarial Network CNN Convolutional Neural Network
CoRGAN Correlation-Capturing GAN DCGAN Deep Convolutional Generative
Adversarial Netwrk DGA Domain Generation Algorithm DL Deep Learning DNN Deep
Neural Network DNS Domain Name Sever DT Decision Tree EMNIST Extended MNIST
database (see MNIST) FID Fréchet Inception Distance FN False Negative FP False
Positive fvGAN Feature Vector Generative Adversarial Network GA Genetic
Algorithm GAN Generative Adversarial Networks GDA Gradient Descent/Ascent
GenAtSeqGAN Generalized Attentive Sequential Generative Adversarial Network
IDS Intrusion Detection System IDS-GAN / IDSGAN Intrusion Detection System GAN
InfoGAN Information Maximising Generative Adversarial Network IS Inception
Scare ISGAN Identity-Sensitive Generative Adversarial Network LeakyReLu Leaky
Rectified Linear Unit LR Logistical Regression LSGAN Least Squares Generative
Adversarial Network LSTM Long Shhort-Term Memory LSTM GAN Long Short-Term
Memory GAN LSTM-CGAN Long Short-Term Memory Conditional Generative Adversarial
Network Mal-IGAN / MaliGAN Maximum-Likelihood Augmented Discrete Generative
Adversarial Net- works Mal-ISGAN (See ISGAN, MalGAN) MCSC-asm Machine
Classification using SimHash and CNN MGAN Mixture Generative Adversarial
Network ML Machine-Learning MLP Multi-Layer Perceptron MNIST Modified National
Institute of Standards and Technology Database MS Mode Score NIC Nature-
Inspired Computing NSGA-II Non-Denominated Sorting Genetic Algorithm PPV
Positive Predictive Value / Also known as Precision ProGAN Proximity
Generative Adversarial Network RF Random Forest SGAN Semi-Supervised
Generative Adversarial Network SVM Support Vector Machine tCDGAN Transferred
Deep-Convolutional Generative Adversarial Network TN True Negative TP True
Positive TPR True Positive Rate VAE Variational Autoencoders WGAN Wasserstein
Generative Adversarial Network WGAN-GP Wasserstein Generative Adversarial
Network with Gradient Penalty