# A Life-long Learning Intrusion Detection System for 6G-Enabled IoV

Abdelaziz Amara korba  LRS, Badji Mokhtar University of Annaba, Algeria  L3I,
University of La Rochelle, France  Souad Sebaa  École nationale Supérieure
d’Informatique, Algeria  Malik Mabrouki  École nationale Supérieure
d’Informatique, Algeria  Yacine Ghamri-Doudane  L3I, University of La
Rochelle, France  Karima Benatchba  École nationale Supérieure d’Informatique,
Algeria

###### Abstract

The introduction of 6G technology into the Internet of Vehicles (IoV) promises
to revolutionize connectivity with ultra-high data rates and seamless network
coverage. However, this technological leap also brings significant challenges,
particularly for the dynamic and diverse IoV landscape, which must meet the
rigorous reliability and security requirements of 6G networks. Furthermore,
integrating 6G will likely increase the IoV’s susceptibility to a spectrum of
emerging cyber threats. Therefore, it is crucial for security mechanisms to
dynamically adapt and learn new attack patterns, keeping pace with the rapid
evolution and diversification of these threats - a capability currently
lacking in existing systems. This paper presents a novel intrusion detection
system leveraging the paradigm of life-long (or continual) learning. Our
methodology combines class-incremental learning with federated learning, an
approach ideally suited to the distributed nature of the IoV. This strategy
effectively harnesses the collective intelligence of Connected and Automated
Vehicles (CAVs) and edge computing capabilities to train the detection system.
To the best of our knowledge, this study is the first to synergize class-
incremental learning with federated learning specifically for cyber attack
detection. Through comprehensive experiments on a recent network traffic
dataset, our system has exhibited a robust adaptability in learning new cyber
attack patterns, while effectively retaining knowledge of previously
encountered ones. Additionally, it has proven to maintain high accuracy and a
low false positive rate.

###### Index Terms:

6G, IoV, Security, intrusion detection, Continual Learning, Life-long
Learning, Federated Learning

##  I Introduction

As we enter the 6G era, the landscape of digital communications and
interactions is undergoing a profound transformation. This evolution is
particularly evident in the Internet of Vehicles (IoV) ecosystem, where the
convergence of 6G technology with Connected and Automated Vehicles (CAVs)
promises to significantly enhance efficiency, convenience, and user
experience. However, this technological advancement is not without its risks.
The adoption of 6G in the IoV potentially increases the vulnerability of CAVs
to a multitude of emerging cyber threats, making security a major concern for
this promising technology [1].

In the complex landscape of cybersecurity, Intrusion Detection Systems (IDS)
hold a critical role. The transition from traditional signature-based IDS to
anomaly-based systems, capitalizing on advancements in artificial intelligence
(AI), represents a significant leap forward. This evolution has led to the
development of IDS that can detect a wide array of cyber attacks with
remarkable precision [2, 3, 4]. Predominantly, these recent AI-driven IDS rely
on deep learning techniques [3, 5], however an inherent limitation of DL: the
challenge of incremental learning from continuously evolving data streams,
often described as catastrophic forgetting [6]. This challenge is particularly
relevant when the system must differentiate between classes that are not
observed concurrently. In essence, the IDS must be capable of dynamically
adapting to new types of attacks or variations in data patterns that were
absent in its initial training dataset. This capability for dynamic learning
and adaptability is a critical component for IDS to remain effective in the
constantly changing landscape of cyber threats.

Continual Learning, also known as life-long learning, focuses on acquiring
knowledge from a continuous stream of data, aiming to expand this knowledge
base without the need for retraining from scratch [6]. This area of ML has
grown due to its practical benefits, such as improving medical diagnoses,
advancing autonomous driving, and accurately predicting financial trends. Its
growth highlights its potential to enhance AI adaptability in diverse real-
world situations [6]. However, few studies [7, 8] have investigated the
application of continual learning for attack detection, the proposed solutions
are not fully suited to the distributed and dynamic nature of the IoV. They
rely on a centralized learning model that requires data collection, which
raises privacy concerns.

This study aims to propose an adaptive IDS capable of learning new attack
patterns while retaining those previously learned. We introduce a detection
system that combines class-incremental learning (CIL) with federated learning
(FL), ensuring adaptability and suitability for the distributed and dynamic
environment of the IoV. To enable evolving detection of emerging cyber
attacks, our approach integrates the Continual Learning with Experience And
Replay (CLEAR) [9] method into our detection model. CLEAR effectively combines
direct learning from recent data, ensuring the system remains adaptable, with
indirect learning from historical data, enhancing its stability. We train this
detection model using FL across local datasets from participating CAVs. In
this setup, Multi-access edge computing (MEC) servers play a pivotal role in
coordinating the training process by acting as parameter servers for
aggregating FL model updates. The effectiveness of the proposed solution is
evaluated using the 5G-NIDD [10] dataset, which contains real-world 5G network
traffic traces. To mimic real-world conditions as closely as possible, we
initially train the model on a dataset containing a single type of attack
mixed with benign traffic. We then progressively introduce samples of various
attack types, each time incorporating a new type. The results have
demonstrably proven the IDS’s ability to learn new patterns while maintaining
high accuracy and a very low False Positive Rate (FPR).

The remainder of this paper is organized as follows. Section II describes
related work. The design of our scheme is presented in Section III. Section IV
depicts the performance evaluation results, and finally, Section V concludes
the paper.

##  II Related Work

Recent studies have increasingly focused on enhancing security within Internet
of Vehicles (IoV) networks. Abdelwahab et al. [11] conducted a comprehensive
study on cybersecurity challenges within the 5G Vehicle-to-Everything (5G-V2X)
networks, addressing issues across 5G infrastructure and specific use cases,
among other security concerns. Osorio et al. [1] provided a thorough analysis
of security and privacy in the 6G-enabled IoV. They explored how key
technological enablers such as network softwarization, blockchain, and AI/ML
enhance secure communication.

Considering Artificial Intelligence’s role, several studies have harnessed
ML/DL to detect and mitigate cyber threats in IoV networks. Rahal et al. [12]
delved into botnet threats within IoV networks. They simulated botnet traffic
to generate a dataset, subsequently applying various machine learning
techniques to detect and analyze botnet traffic. To address the challenges
associated with zero-day attack detection in IoV networks, in our previous
work [3], we proposed a federated learning (FL)-based Intrusion Detection
System (IDS) utilizing a deep autoencoder. To overcome the limitations of
N-day attack recognition and the constraints associated with centralized FL,
we extended this work [4] by leveraging the paradigm of open-set learning with
blockchain-enabled FL.

To address the challenges associated with deploying machine learning in
6G-enabled IoV, Hoang et al. [5] proposed a secure and reliable integration of
Transfer Learning into the 6G-enabled IoV framework. Addressing security
threats in 6G IoV networks, Sedjlmaci et al. [2] developed a collaborative
cybersecurity framework based on a multi-level FL algorithm and Stackelberg
security games. In a similar vein, Zhang et al. [13] designed a sophisticated
weight-based ensemble ML algorithm, optimized with many-objective techniques,
for identifying anomalies in vehicular Controller Area Network (CAN) bus
systems, thereby aligning with the high security demands of 6G networks.
However, while these studies propose IDS solutions suitable for 6G-enabled
IoV, they do not fully address the adaptability challenges in a such dynamic
and constantly evolving environments where cyber threats are in continuous
flux, with new ones emerging. A notable limitation of DL-based IDS is their
struggle with incremental learning from continuously evolving data streams, a
problem often termed as catastrophic forgetting [6].

To tackle the challenge of adapting IDS to new attack patterns and addressing
catastrophic forgetting in DL-based IDS, the study by Prasath et al. [8]
examines the effectiveness of continual learning models for the incremental
learning of novel attack patterns. This research involved both experimental
and analytical studies, focusing on three key continual learning methods:
learning without forgetting, experience replay, and dark experience replay.
Additionally, another significant effort in employing continual learning is
the work by Ejaz et al. [7], which investigates the use of continual learning
techniques for consistent phishing detection over time. This study trained a
vanilla neural network (VNN) model with deep feature embedding of HTML content
in a continual learning setup. The results indicate that continual learning
algorithms effectively maintain accuracy over time, albeit with slight
performance decline. However, these solutions predominantly utilize
centralized learning models, which may not align with the specific demands of
the IoV environment. These models often require high bandwidth and result in
increased latency. Moreover, there are privacy concerns, as sensitive data
might be compromised during transmission to central locations.

To align with the specific demands of the IoV environment, recent studies [14,
15] have proposed Federated Learning (FL)-based IDS for misbehavior detection
in 5G and 6G-enabled IoV. Our solution uniquely combines FL and Continual
Learning (CL) to ensure both adaptability and suitability within the
distributed and dynamic environment of the IoV.

##  III Proposed Solution

Figure 1: MEC-Enabled CIL Federated learning architecture

The proposed Intrusion Detection System (IDS) is specifically designed for
deployment in Connected and Automated Vehicles (CAVs), where it actively
monitors network traffic. Our detection model uses class-incremental learning
to adapt to evolving attack patterns. Additionally, federated learning is
employed to facilitate distributed and privacy-preserving training. In our
system architecture, the Multi-access Edge Computing (MEC) server functions as
the parameter server, with the CAVs acting as client nodes, as depicted in
Figure 1. We detail the development process of the IDS in the following
sections.

###  III-A Network Traffic Processing

The traffic collector module of the IDS converts raw captured packets into
flows, thereby enhancing the understanding of traffic patterns. To
characterize each flow, a set of features is calculated based on packet header
analysis, which includes data from both the network and transport layers of
the network architecture. These network features can be categorized into four
main types: Packet-Based features focus on metrics such as packet counts and
transmission rates, providing insights into traffic volume and flow patterns.
Byte-Based features analyze the volume of transmitted data to assess network
load and bandwidth usage. Time-Based features capture the temporal
characteristics of traffic, including flow durations and intervals between
transmissions, aiding in the identification of activity timing and anomaly
detection. Protocol-Based features leverage specific protocol information to
distinguish between different types of network traffic. The resulting dataset
of network flows is then utilized by the CAV for local training of the
detection model.

###  III-B Federated Attack Incremental Learning

To enable continual learning of the detection model, we use the Continual
Learning with Experience And Replay (CLEAR) [9] method for our detection
model. This methods fuses direct learning from new experiences to preserve the
system’s adaptability with indirect learning from past experiences to bolster
stability. Moreover, CLEAR strengthens the system’s consistency by
incorporating behavioral cloning, aligning the current operational guidelines
with prior iterations. CLEAR is underpinned by an actor-critic training regime
that leverages both fresh and historical experiences. Formally, the network
parameters are denoted by $\theta$, with $\pi_{\theta}$ indicating the
network’s current policy over actions $a$, and $h_{s}$ representing the hidden
state of the network at time $s$. The policy generating the observed
experience is represented by $\mu$. The $V$-Trace target $v_{s}$ for this
method is defined as follows [9]:

$v_{s}:=V(h_{s})+\sum_{t=s}^{s+n-1}\gamma^{t-s}\left(\prod_{i=s}^{t-1}c_{i}\right)\delta
V_{t},$

where $\delta V_{t}:=\rho_{t}(r_{t}+\gamma V(h_{t+1})-V(h_{t}))$ for truncated
importance sampling weights
$c_{i}:=\min\left(\overline{c},\frac{\pi_{\theta}(a_{i}|h_{i})}{\mu(a_{i}|h_{i})}\right)$,
and
$\rho_{t}:=\min\left(\overline{\rho},\frac{\pi_{\theta}(a_{t}|h_{t})}{\mu(a_{t}|h_{t})}\right)$
(with $\overline{c}$ and $\overline{\rho}$ constants). The policy gradient
loss is:

$L_{\text{policy-
gradient}}:=-\rho_{s}\log\pi_{\theta}(a_{s}|h_{s})(r_{s}+\gamma
v_{s+1}-V(h_{s})).$

The loss functions $L_{\text{policy-gradient}}$, $L_{\text{value}}$, and
$L_{\text{entropy}}$ are utilized for both new and replay experiences.
Additionally, $L_{\text{policy-cloning}}$ and $L_{\text{value-cloning}}$ are
incorporated exclusively for replay experiences.

In replay experiences, additional loss terms are incorporated to enable
behavioral cloning between the network’s current state and its historical
counterparts. This is aimed at preventing deviations in the network’s output
on replayed tasks during the learning of new tasks. The methodology includes
penalizing (1) the Kullback-Leibler (KL) divergence, which assesses the
disparity between the historical and present policy distributions, and (2) the
L2 norm, reflecting the variance between historical and current value
functions. Technically, this leads to the integration of specific loss
functions [9]:

$L_{\text{policy-
cloning}}:=\sum_{a}\mu(a|h_{s})\log\frac{\mu(a|h_{s})}{\pi_{\theta}(a|h_{s})},$
$L_{\text{value-
cloning}}:=\|V(\theta_{h_{s}})-V_{\text{replay}}(h_{s})\|^{2}.$

1 Variables: K: index of clients, B: local batch size, E: number of local
epochs, $\eta$ : learning rate

2

3Initialize ${w}_{0}$

4 for  _each round $t\in\\{1,...,N\\}$_ do

5 $m\leftarrow$ max(C. K, 1)

6 $S_{t}\leftarrow$ (random set of m CAVs)

7 for  _each CAV $k\in S_{t}$ in parallel_ do

8 $\beta_{k}\leftarrow$ (split $\mathit{P_{k}}$ into batch of size
$\mathit{B}$)

9 for  _each local epoch $i\in\\{1,...,E\\}$_ do

10 for  _batch $b\in\beta_{k}$_ do

11 $w_{k}\leftarrow\textit{w}_{k}-\eta\nabla l(w_{k};b)$

12

13 end for

14

15 end for

16 $w_{t+1}^{k}\leftarrow w_{k}$

17

18 end for

19 $w_{t+1}\leftarrow\sum_{k=1}^{n}\frac{n_{k}}{n}w_{t+1}^{k}$

20

21 end for

Algorithm 1 Federated Averaging Algorithm

The MEC nodes orchestrate the federated training process of the detection
model by playing the role of parameters servers for model updates aggregation.
First, the MEC server initializes the learning parameters of the detection
model (number of layers, number of neurons, activation functions,learning
rate, etc.). It then shares such parameters with the participant CAVs. Each
CAV trains the detection model on its local dataset. Once done, each CAV sends
its local model’s parameters (weights) to the MEC server. The latter
aggregates the received local models to generate a global learning model,
before sending it back to the involved CAVs, in order to initiate a new
training round. Algorithm 1 illustrates the main steps performed by both the
MEC server and each participating CAV. In our study, the federated learning
problem across multiple CAVs is formulated as a federated optimization problem
and resolved using the FedAvg algorithm [16]. Indeed, using its local data,
each CAV calculates the average gradient on top of the model $w$ for a
corresponding training round $r$. Thereafter, each CAV performs a local
gradient descent on the currently used model with its own data. On the other
hand, the MEC server aggregates these local updates and transfers back the
global model to the CAV collaborators. This process is repeated during a
number of rounds, defined initially by the MEC server.

##  IV Performance Evaluation

###  IV-A Dataset

We evaluated our system using the 5G Network-Intrusion Detection and Defense
(5G-NIDD) [10] dataset. This dataset was selected for its recency, the variety
of attack types it includes, and particularly because it contains real 5G
network traffic. Ideally, we would have preferred to use a dataset with 6G
traffic, but to our knowledge, such a dataset is not yet available. The
dataset includes examples of Denial of Service (DoS) attacks, such as ICMP
Flood, UDP Flood, SYN Flood, HTTP Flood, and Slowrate DoS, as well as port
scans, including SYN Scan, TCP Connect Scan, and UDP Scan. Following a
comprehensive data preprocessing that involved cleaning, normalization, and
feature engineering, the final dataset was refined to include 81 features. The
sample distribution of network traffic per attack is presented in Table I.

Figure 2: Evaluation of Performance Throughout the Incremental Learning
Process

(a)

(b)

Figure 3: Accuracy vs. Nb. clients & Nb. Iterations

###  IV-B Experimental results

We first assess our detection model’s performance within a centralized
training setup. Subsequently, we proceed to evaluate its accuracy in a
federated training setup. To ensure a validation as realistic and closely
aligned with real-world scenarios as possible, we begin by training the model
on a dataset that includes only one type of attack along with benign traffic.
Subsequently, we sequentially introduce samples of different attack types,
each time integrating a new type. The order of the attacks’ introduction is
completely random. The model evaluation was based on the three metrics
detailed below:

  * •

Accuracy: the proportion of correctly identified samples (both true positives
and true negatives) to the total number of observations.

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

  * •

Recall: measures the proportion of actual positives that are correctly
identified.

$Recall=\frac{TP}{TP+FN}$ (2)

  * •

FPR (False Positive Rate): Measures the proportion of actual negatives that
are incorrectly identified as positives.

$FPR=\frac{FP}{FP+TN}$ (3)

  * •

F1-Score: The harmonic mean of Precision and Recall.

$F1\\_Score=2\times\frac{Precision\times Recall}{Precision+Recall}$ (4)

TP, TN, FP, and FN denote true positive, true negative, false positive, and
false negative, respectively.

TABLE I: Dataset samples distribution Attack | Absolute count | Fraction (%)
---|---|---
Benign | 477737 | 39.29
UDPFlood | 457340 | 37.61
HTTPFlood | 140812 | 11.58
SlowrateDoS | 73124 | 6.01
TCPConScan | 20052 | 1.65
SYNScan | 20043 | 1.65
UDPScan | 15906 | 1.31
SYNFlood | 9721 | 0.80
ICMPFlood | 1155 | 0.09

It. locales | Multiclass Acc. | Macro Recall | Weighted Recall | Binary Acc. | Binary FPR | Binary Precision | Binary Recall | Binary F1-Score
---|---|---|---|---|---|---|---|---
100 | 0.839 | 0.642 | 0.839 | 0.960 | 0.025 | 0.969 | 0.941 | 0.955
200 | 0.919 | 0.854 | 0.919 | 0.980 | 0.014 | 0.983 | 0.973 | 0.978
300 | 0.931 | 0.874 | 0.931 | 0.985 | 0.020 | 0.976 | 0.991 | 0.984
400 | 0.926 | 0.875 | 0.926 | 0.980 | 0.031 | 0.964 | 0.993 | 0.978
500 | 0.921 | 0.874 | 0.921 | 0.975 | 0.040 | 0.954 | 0.993 | 0.973

TABLE II: No. of Local Iterations vs. Predictive performances

No. of Clients | Multiclass Acc. | Macro Recall | Weighted Recall | Binary Acc. | Binary FPR | Binary Precision | Binary Recall | Binary F1-Score
---|---|---|---|---|---|---|---|---
20 | 0.928 | 0.874 | 0.928 | 0.984 | 0.024 | 0.972 | 0.993 | 0.982
40 | 0.933 | 0.874 | 0.933 | 0.987 | 0.016 | 0.980 | 0.991 | 0.986
60 | 0.923 | 0.870 | 0.923 | 0.979 | 0.033 | 0.961 | 0.993 | 0.977
80 | 0.927 | 0.872 | 0.927 | 0.984 | 0.024 | 0.972 | 0.993 | 0.982

TABLE III: Nb. of Clients vs. Predictive performances

####  IV-B1 Centralized training

We implemented and evaluated our proposed system within the Google Colab cloud
environment, utilizing the Pytorch package to implement both local and
federated learning models. The core of our system is a Multilayer Perceptron
(MLP) model, intricately structured with three hidden layers. Each layer
comprises 300 neurons, activated by the ReLU (Rectified Linear Unit) function.
Throughout its training phase, it undergoes 1000 iterations. Additionally, we
incorporated a buffer memory capable of storing up to 100 samples. This memory
plays a crucial role in our implementation of CLEAR method. By preserving
previously observed samples, it significantly mitigates the issue of
catastrophic forgetting.

Figure 2 illustrates the performance dynamics as new types of attacks were
incrementally introduced to the system. Initially, the system’s handling of
benign traffic and Lowrate DoS attack was exemplary, achieving 100% accuracy,
a 0% FPR, and a detection rate of over 99%. Despite a slight decline in
performance following the addition of UDP Flood and HTTP Flood attacks, the
model successfully maintained high levels of performance. Upon the final
integration of all attack types, the detection model sustained robust
performance, with both accuracy and recall exceeding 92%, while maintaining a
very low FPR of 2%.

####  IV-B2 Federated training

In our federated training approach, we implemented an independent and
identically distributed (IID) sampling setup. We followed the same testing
strategy as our previous experiment, which involves both sequential and random
observations of various attack types. In our initial setup, we considered 10
clients and conducted training over 10 rounds. We experimented with varying
numbers of local iterations to assess the model’s convergence. As shown in
Figure 3(a), it is evident that the model converges after 300 local iterations
(across 10 FL rounds). This observation is corroborated by the swift learning
rate and consistently high accuracy depicted in the plot.

For a comprehensive evaluation, we assessed the system in both binary
classification (benign, malicious) and multi-class classification scenarios.
In the multi-class context, we considered both Macro Recall and Weighted
Recall. Macro Recall measures the average performance across all classes,
treating each class equally. Weighted Recall, on the other hand, accounts for
the frequency of each class, providing a more realistic evaluation in
scenarios where some attacks are more prevalent than others. These metrics
offer a well-rounded assessment, considering both majority and minority
attacks, providing a comprehensive perspective on the model’s capabilities and
opportunities for improvement. As illustrated in Table II, the detection model
shows very good performances with a high weighted recall of 0.931, showcasing
its strong performance in identifying majority classes. While the macro recall
is at 0.874, this still represents a commendable performance, especially in
the context of less frequent classes.

To assess the system’s scalability, we experimented with varying the numbers
of FL CAVs. Figure 3(b) indicates strong scalability in the federated learning
process. Despite increasing the number of clients per MEC node, from 20 to 80,
there is no significant variance in accuracy, suggesting that the system can
handle scaling horizontally—adding more clients—without a loss in performance.
The quick convergence and stable high accuracy across all client
configurations demonstrate the system’s robustness and effectiveness in a
distributed learning context. Table III demonstrates that the detection model
maintains stable and high performance in both multiclass and binary
classifications across varying numbers of clients. The model achieves high
accuracy and precision, indicating its robustness. While the macro recall
indicates a slightly lower performance in identifying less frequent attack
types, the high F1-scores across all client groups suggest a well-balanced
model. The consistent performance, regardless of the number of clients,
affirms the model’s scalability and effectiveness.

##  V Conclusion

This study introduced a new adaptive IDS designed for the constantly evolving
IoV security environment, in anticipation of the upcoming 6G technology shift.
By integrating class-incremental and federated learning, which suited the
IoV’s distributed structure, the system achieved high accuracy and a low false
positive rate, as demonstrated in our tests using a recent dataset. These
results emphasized the system’s flexibility and marked a step forward in AI-
driven cybersecurity for vehicular networks. Our efforts provided a
foundational step for enhancing IoV security against the new wave of cyber
threats expected with 6G advancements.

## Acknowledgment

This work was supported by the 5G-INSIGHT bilateral project (ID: 14891397) /
(ANR-20-CE25-0015-16), funded by the Luxembourg National Research Fund (FNR),
and by the French National Research Agency (ANR).

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