# ADVENT: Attack/Anomaly Detection in VANETs

Hamideh Baharlouei  Computer Science, Dalhousie University, Halifax, NS,
Canada.  Corresponding author(s). E-mail(s<EMAIL_ADDRESS>Tokunbo Makanju
New York Institute of Technology - Vancouver Campus, Vancouver, BC, Canada.
Nur Zincir-Heywood  Computer Science, Dalhousie University, Halifax, NS,
Canada.

###### Abstract

In the domain of Vehicular Ad hoc Networks (VANETs), where the imperative of
having a real-world malicious detector capable of detecting attacks in real-
time and unveiling their perpetrators is crucial, our study introduces a
system with this goal. This system is designed for real-time detection of
malicious behavior, addressing the critical need to first identify the onset
of attacks and subsequently the responsible actors. Prior work in this area
have never addressed both requirements, which we believe are necessary for
real world deployment, simultaneously. By seamlessly integrating statistical
and machine learning techniques, the proposed system prioritizes simplicity
and efficiency. It excels in swiftly detecting attack onsets with a remarkable
F1-score of 99.66%, subsequently identifying malicious vehicles with an
average F1-score of approximately 97.85%. Incorporating federated learning in
both stages enhances privacy and improves the efficiency of malicious node
detection, effectively reducing the false negative rate.

Keywords:VANET, Anomaly Detection, Malicious Behaviour Detection, Machine
Learning, Federated Learning, and Privacy.

##  1 Introduction

Vehicular Ad hoc Networks (VANETs) as a specific type of mobile ad-hoc
networks (MANET) are commonly associated with transportation systems with
three main channels: vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), and
vehicle-to-infrastructure (V2I), collectively known as V2X (vehicle-to-
everything). VANET communications provide real-time information to tackle
delayed driver responses. This enables immediate control over vehicle
functions like brakes, acceleration, and steering. It offers advantages such
as contributing to traffic safety by delivering precise information directly
to drivers. However, the dynamic nature of VANETs, marked by constantly
changing network topologies, varying vehicle speeds, and differences in the
density of V2X communications, introduces new challenges and vulnerabilities
that must be addressed [1]. These vulnerabilities can be exploited to launch
various types of attacks, which could result in various issues such as
accidents and traffic congestion. Thus, ensuring the security of VANETs is of
great significance due to the potential risks to human lives, property, and
economic activities. This underscores the need to prioritize the development
of robust information system security tools and mechanisms capable of not only
detecting but also effectively mitigating these attacks. Taking proactive
measures is essential to ensure the integrity and safety of VANETs in the face
of the evolving cybersecurity threats.[1, 2, 3]

Unfortunately, conducting research in these fields through the deployment and
testing of actual VANETs often entails significant expense and labour, making
it impractical in many instances. Therefore, simulation tools are widely
employed to evaluate strategies before the actual implementation of various
algorithms and methodologies [4]. In this study, we utilized datasets
generated via simulations, as outlined in [5]. These simulations take into
account three main factors to ensure the reliability of the results for real-
world applicability. The first factor is the grid pattern. To replicate
scenarios in our simulations, we leverage real geographical maps and
topological data from multiple cities with different grid patterns. These
different grid patterns can impact vehicle behavior, movements, and
communication. This approach provides a better understanding of how the
proposed system could work in the real-world conditions as it has been tested
in city road networks with different grid patterns. The second factor
considered is the integration of realistic communication settings within the
simulations. This directly influences how data packets are transmitted and
received, aligning the simulations more closely with real-world conditions.
The third and final factor revolves around the mobility patterns of vehicles.
This encompasses various aspects, including vehicle movement patterns, the
number of vehicles in each scenario, their entry into the simulation
(including time intervals), their trajectories, and their interactions within
the network. In this research, these aspects are designed to mirror real-world
vehicle behaviors and movements, thereby also enhancing the practicality of
the simulation results [6, 5].

DDoS attacks pose a significant threat to the reliability and functionality of
VANETs, as these networks heavily rely on the exchange of real-time
information among vehicles and infrastructure to enhance road safety and
efficiency [7, 8]. By flooding the network with an overwhelming volume of
malicious traffic or by exploiting vulnerabilities in the network
infrastructure, attackers can exhaust network resources, degrade the quality
of service, and impede the transmission of vital safety messages. This can
lead to delays, loss of critical information, and compromised situational
awareness among connected vehicles, ultimately endangering the safety of
drivers and passengers. Addressing DDoS attacks in VANETs is thus crucial, as
it ensures the network’s integrity, reliability, and uninterrupted
communication, all essential for safe and efficient transportation [9].

In this paper, we propose a real-time malicious behavior detector which
operates in three distinct steps that include, data preprocessing, the
detection of the onset of an attack and the identification of the malicious
nodes involved. It combines both Machine Learning (ML) and statistical
techniques to leverage the advantages of each method. Furthermore, it ensures
crucial data privacy, a significant concern in all ML-based approaches,
especially when training with data collected from various nodes or locations
and sharing it with other nodes in the network. This privacy is achieved
through the integration of federated learning [10]. Federated learning is
seamlessly integrated into both the machine learning and statistical
approaches for identifying attack onsets and malicious nodes. This integration
allows us to avoid transmitting raw data to other nodes in the network,
instead, sharing only trees and lists. Additionally, the inclusion of
Federated Learning significantly improves efficiency and notably reduces the
False Negative Rate (FNR), particularly in the malicious node detection phase,
while preserving crucial privacy. It’s worth emphasizing that while ML-based
approaches offer advantages, they come with certain challenges as well. The
effectiveness of ML algorithms relies heavily on the quality of the training
data, preprocessing and feature selection. These challenges, if not properly
addressed, can lead to faulty and unnecessarily complex models.

To address these challenges, we have introduced an innovative preprocessing
method. This method generates training data that captures the number of
packets received by each node in every second within the VANET network. By
adopting this approach, we gain a more comprehensive understanding of
communication patterns, potential attack scenarios, and facilitate real-time
operation. In particular, this method reduces the dataset’s size and
processing time, contributing to a more efficient exploration of network
dynamics. Consequently, it enhances our capabilities for effective and
efficient attack detection in VANETs. Additionally, we are able to determine a
threshold for the ratio of benign to malicious samples required in data for
local model training. Our proposed system employs this threshold value and
Synthetic Minority Oversampling Technique (SMOTE) [11] to achieve the desired
ratio in all local training samples. This has proven instrumental in obtaining
improved results and minimizing the False Negative Rate (FNR). In summary,
this research introduces five novel contributions:

  1. 1.

Designing and developing a comprehensive three-tier real-time system that
effectively detects the onset of an attack and identifies the malicious nodes
responsible.

  2. 2.

Implementing a Federated Learning based approach for enhaning privacy, while
simultaneously leveraging the collective experience of all nodes to boost the
efficiency and reduce the False Negative Rate (FNR).

  3. 3.

Prioritizing simplicity and efficiency through the feature selection process,
which combines domain expertise with innovative feature engineering
techniques. This enables the selection of the most informative features,
increasing the ability of the proposed system to detect attacks in VANETs.

  4. 4.

Bolstering the robustness of the proposed system via datasets that are
generated in realistic VANET simulations, with a focus on geographical and
traffic diversity. Thus, unlike prior work [12], datasets employed represent
the VANET dynamics more accurately. The use of various geographical maps and
simulation factors ensures the reliability of data, which serves as a strong
foundation for training and evaluating the proposed system [5, 13]

  5. 5.

Addressing the challenge of imbalanced data and the scarcity of malicious
samples in local dataset via threshold identified for mitigation.
Subsequently, the Synthetic Minority Over-sampling Technique (SMOTE) is
implemented based on this threshold. This strategy aims to balance the dataset
by generating synthetic samples, particularly focusing on the minority class,
and therefore, contributing to more robust and representative training data
for the proposed system.

The rest of this paper is organized as follows: Section 2 discusses previous
works. Section 3 describes the components of the proposed system and how they
work. Simulations and results are presented in Section 4. Finally, conclusions
are drawn and future research directions are discussed in Section 5.

##  2 Related work

In this section, a comprehensive overview of previous studies in the domain of
Vehicular Ad hoc Networks (VANETs) is presented, with a specific focus on
three key aspects: the general landscape of VANETs, the application of machine
learning for attack detection in VANETs, and the exploration of Federated
Learning within the context of VANETs.

###  2.1 VANETs

Cavalcanti et al. [14] extensively reviewed a decade of VANET research
(2007-2016) in their analysis of 283 scholarly works. Their findings
highlighted the enduring prevalence of simulation as the primary validation
tool for solutions in MANETs. Over 75% of papers consistently utilized
simulation, a trend that has persisted since 2000-2005. The study also noted a
shift from Network Simulator 2 (NS-2) to OMNET++, with a 19.5% decrease in
NS-2 usage and a more than twofold increase in OMNET++ adoption, from 5% to
12%. Moreover, Cavalcanti et al. explored choices of simulation tools for
vehicular networks, identifying SUMO as the most widely used (31.2%), followed
by VanetMobiSim at 11.8%. However, a significant 42.4% of simulation-based
papers did not specify the mobility tool used, indicating the diverse and
evolving landscape of VANET research methodologies.

###  2.2 Using Statistical Techniques for Malicious Node Detection

Valentini et al. [15] proposed MDASTI (Attacks Detection Mechanism for
Intelligent Transport System) as a host-based attack detection mechanism for
intelligent transport systems, focusing on ARP protocol data. Their approach
utilized the MAD statistic for identifying potentially malicious nodes,
particularly in the context of Sao Paolo, where they calculated a threshold
for the number of normal packets received from each neighbor and made a list
of malicious nodes.

Baharlouei et al. [5] made contributions to the field by leveraging Malicious
Activity Detection (MAD) to characterize Distributed Denial of Service (DDoS)
attacks. Their research delved into the intricacies of DDoS attacks across a
spectrum of urban scenarios, considering factors such as packet transmission
ranges and the number of vehicles involved in different scenarios. Their
findings show the critical importance of conducting realistic simulations,
particularly when evaluating Intrusion Detection Systems (IDSs). In the realm
of cybersecurity, where precision and reliability are paramount, the need for
realistic simulations cannot be overstated. Valentini et al. [15] and
Baharlouei et al. [5] have made notable contributions to vehicular network
security by preparing datasets and detecting malicious nodes. But their work
did not encompass the critical aspects of attack onset detection and privacy
preservation. These are significant facets that this work aims to address and
enhance, thereby providing a more comprehensive and robust solution for
vehicular network security.

###  2.3 Using Machine learning for Attack Detection

Alheeti et al. [16] introduced an intelligent intrusion detection system (IDS)
that leveraged back-propagation neural networks for identifying Denial of
Service (DoS) attacks. Their approach, which relied on the Kyoto 2006+
dataset, incorporated ensemble learning and feature selection to achieve a
commendable balance between high detection rates and low computational costs.

Rani et al. [17], on the other hand, proposed an intelligent transport system
tailored to Internet of Vehicles (IoV)-based vehicular network traffic. Their
system harnessed tree-based models, including Decision Tree (DT), Random
Forest (RF), Extra Tree (ET), and XGBoost, demonstrating substantial detection
accuracy and computational efficiency on the ”CIC-IDS2017” dataset by
employing ensemble learning and essential feature selection techniques.

Aloqaily et al. [18] introduced an IDS for IoV and connected vehicles, relying
on deep belief networks (DBN) and decision tree (DT) algorithms. Their system
exhibited impressive accuracy, as validated using the NSL-KDD dataset.

Gao et al. [19] addressed the challenge of distributed Denial of Service
(DDoS) network intrusion detection. Their system was composed of two key
modules: a real-time network traffic collection module and a network traffic
detection module. The latter employed a classification algorithm based on
Random Forest (RF) and was evaluated and compared using datasets that included
NSL-KDD and UNSW-NB15.

Additionally, Injadat et al. [20] devised a multi-stage, optimized machine
learning-based IDS specifically tailored for network attack detection. Their
performance evaluations were conducted on the CICIDS2017 and UNSW-NB15
datasets, where they leveraged hyper-parameter optimization (HPO) techniques
to fine-tune machine learning models.

Li et al. [21]conducted a thorough survey on Controller Area Networks (CAN)
and Autonomous Vehicle (AV) networks’ vulnerabilities, culminating in an
Intrusion Detection System (IDS) designed for both contexts. They harnessed
four tree-based supervised learning algorithms and integrated a stacking
ensemble model to enhance intrusion detection accuracy while optimizing
computational efficiency. Evaluation using public datasets demonstrated the
IDS’s high accuracy in safeguarding CAN and AV networks, emphasizing the
efficacy of tree-based supervised learning in Internet of Vehicles and
autonomous driving security. Previous studies, such as those by Alheeti et al.
[16], Rani et al. [17], Aloqaily et al. [18], Gao et al. [19], Injadat et al.
[20] and Li et al. [21], have explored various intrusion detection methods for
vehicular networks. These studies encompassed a wide array of techniques, from
back-propagation neural networks to tree-based models and used different
datasets for evaluation. The proposed work not only builds upon this prior
research but also fills a critical gap in vehicular network security by
focusing on attack onset detection, malicious node identification, and robust
data privacy techniques, all while maintaining model efficiency and
effectiveness and providing evaluations based on data from VANETS.

In a recent work, Li et al. [12]introduced a robust multi-tiered hybrid
Intrusion Detection System (MTH-IDS) for IoV environments. This IDS
effectively safeguards intra-vehicle and external networks from diverse attack
types, both known and unknown, operating in real-time scenarios. MTH-IDS
involves data pre-processing, feature engineering, and four tiers of learning
models, addressing various aspects of known and unknown attack detection.
Evaluation using well-established datasets (CAN-intrusion-dataset[22] and
CICIDS2017-dataset[23]) demonstrated MTH-IDS’s remarkable performance in
detecting unknown attacks, maintaining a high detection rate with a low false
alarm rate. This research underscores MTH-IDS’s potential as a robust and
versatile intrusion detection system for Internet of Vehicles environments.

Baharlouei et al. [13] introduced an approach for detecting malicious behavior
in VANETs, covering both attack onset and identification of malicious nodes.
They utilized a real VANET dataset, incorporating realistic simulation factors
and introducing a novel data preprocessing method. The authors employed four
tree-based models to construct an ensemble model, optimizing parameters with
BO-TPE (Bayesian Optimization with Tree-structured Parzen Estimator). They
implemented a malicious node detection mechanism using the MAD method,
achieving a 100% Detection Rate in the Attack Onset detection step and an
average of 90% in the Malicious Node detection step.

###  2.4 Federated Learning

Cheng et al. [24] address the critical concern of user privacy protection in
machine learning and propose SecureBoost. This innovative federated learning
system achieves privacy preservation by conducting entity alignment through a
privacy-preserving protocol and subsequently constructing boosting trees
across multiple parties using a meticulously designed encryption strategy.
SecureBoost operates on vertically partitioned datasets, where multiple
parties collaborate in the learning process while maintaining distinct feature
sets.

Ma et al. [10]address the privacy challenges associated with decentralized
datasets and leverages the strength of extreme Gradient Boosting (XGBoost) in
handling tabular data within the context of federated learning (FL). Notably,
they tackle a specific aspect of FL, horizontal federated XGBoost, leading to
heightened per-node communication frequencies and substantial privacy
concerns. To mitigate these issues, Ma et al. introduce an innovative system
that removes the dependency on gradient sharing. Instead, they make the
learning rates of aggregated tree ensembles learnable, simultaneously
enhancing privacy and communication efficiency. Through extensive evaluations
on diverse datasets for classification and regression tasks, their approach
demonstrates performance comparable to state-of-the-art methods. Moreover, it
significantly enhances communication efficiency by reducing both communication
rounds and overhead.

###  2.5 Drawbacks of Prior Work

While [12, 21] represents the state-of-the-art, Yang et al. dedicated their
efforts to the critical task of attack detection, although they did not
specifically address malicious node detection. Their research involved the
utilization of two datasets, one for internal vehicle networks and another for
external network traffic. However, it is worth noting that their work did not
include simulations of vehicular network traffic. Valentini et al. addressed
malicious node detection in [15] but overlooked the onset of an attack, using
only one dataset from Sao Paolo. None of these papers incorporated federated
learning in VANET security or attack detection. Privacy concerns were
insufficiently addressed, especially during training, where entire datasets
were employed without considering privacy implications. In the real world,
using actual vehicle datasets for training raises significant privacy issues.
Moreover, none discussed incorporating other vehicles’ experiences in
malicious node detection. By leveraging federated learning, we offer the
benefits of shared experiences without compromising privacy.

While they also utilized Synthetic Minority Over-sampling Technique
(SMOTE)[11], it’s noteworthy that our approach distinguishes itself by
incorporating a specific threshold and a method for applying SMOTE based on
this identified threshold. By establishing a relationship between the required
number of samples for efficient model training, we have implemented a targeted
and informed use of SMOTE. This methodological nuance enhances the
adaptability and effectiveness of our data augmentation strategy, addressing
imbalanced datasets more precisely for improved model performance.

In [5], Baharlouei et al. focused on VANET simulations and detecting malicious
nodes. However, they did not study the detection of the onset of an attack
either. In [13], Baharlouei et al. delved into real-time malicious behavior
detection, identifying the onset of attacks and malicious nodes. However, two
significant unresolved issues emerged. Firstly, they did not address privacy
concerns when utilizing raw data from all nodes for model training, a critical
matter especially when dealing with data from real word cyber-phyiscal systems
that might not be owned or controlled by the same entity. Secondly, there was
a notable variation in the False Negative Rate (FNR) during malicious node
detection due to differences in how each node experienced the attack. Based on
their position in the grid and the length of time they remained in the grid
during the attack, some nodes were only partially impacted by the effects of
the attack. This diversity posed challenges in determining the optimal
threshold and identifying malicious nodes. In this context, the development of
a method allowing the simultaneous utilization of all nodes’ experiences could
mitigate these issues arising from topological and geographical disparities.
Additionally, leveraging federated learning for decentralized model training
proved instrumental in streamlining our approach. This approach reduced the
number of models to just one in the attack onset detection step and eliminated
the need for an optimization step. Both of these adjustments effectively
reduced complexity and enhanced overall performance, enabling the development
of a real-time method.

Table 1: Comparative Analysis of Contemporary Intrusion Detection Methods for Vehicular Ad-Hoc Networks (VANETs) Paper Authors | VD | AD | DOA | MND | FE | SL | FL | DP | AM | OT | ID
---|---|---|---|---|---|---|---|---|---|---|---
Valentini et al.[15] | * |  |  | * | * |  |  |  |  |  |
Yang et al. [21] |  | * |  |  | * |  |  |  |  |  |
Yang et al. [12] |  | * |  |  | * | * |  |  |  | * | *
Cheng et al. [24] |  |  |  |  |  |  | * | * | * |  |
Ma et al. [10] |  |  |  |  |  |  | * | * | * |  |
Baharlouei et al.[13] | * | * | * | * | * | * |  |  |  | * |
Proposed System | * | * | * | * | * | * | * | * | * | * | *

In Table 1, we provide a comprehensive summary that compares the work
presented in this paper to some of the most relevant prior works mentioned
earlier. The comparison is carried out using what we consider the key
components for evaluating research into Intrusion Detection System (IDS) for
Vehicular Ad Hoc Networks (VANETs) and the components the system should have
to be ready for real world deployment. The aspects we considered including,
the utilization of VANET datasets(VD), attack detection(AD), capability in
detecting the onset of attacks(DOA), identifying malicious nodes(MND),
incorporation of feature engineering(FE), application of supervised
learning(SL), integration of Federated Learning(FL), provision of data privacy
measures(DP), utilization of aggregation methods(AM), optimization
techniques(OT) employed, and the extent to which each method addresses issues
related to imbalanced data(ID). This comparative analysis provides a holistic
overview of the strengths and limitations of previous approaches in the field.
In summary our research presents a novel feature representation method and a
multi-layered real-time approach capable of detecting the initiation of a DDoS
attack and identifying the responsible malicious nodes. We prioritize data
privacy for all nodes by leveraging federated learning techniques, sharing
only trees instead of raw data during attack onset detection and lists during
malicious node detection. Additionally, we conduct evaluations using four
distinct vehicular network datasets that represent real-world scenarios,
providing a comprehensive assessment of our proposed approach.

##  3 Methodology/System Overview

In this paper, a multi-layered real-time malicious behavior detection system
is proposed. The system aims to identify the onset of DDoS attacks and,
subsequently, the malicious vehicles involved. The system architecture,
illustrated in Figure 1, encapsulates the essential components that
collaboratively contribute to achieving our objectives. In the following
sections, we delve into the details of these components and elucidate how the
system operates under various conditions.

###  3.1 Components of the System

The proposed system comprises three main components: nodes (vehicles), server
in the cloud, and communication links connecting them. All of these are
described in this section.

####  3.1.1 Nodes (Vehicles)

Upon joining the network, each vehicle initiates data processing using the
proposed system, creating its labeled dataset from observed data streams.
During regular operation, there is no need for the Malicious Node Detection
module. However, vehicles periodically train new attack onset detection
models, sharing them with the server in the cloud at predetermined intervals
to maintain an updated model across the network. Additionally, all nodes
continue to engage in regular V2V communication to exchange information.

####  3.1.2 Central Server in the Cloud

The central server plays a crucial role in the system’s initialization process
by acquiring initial trees from simulated or real-world data during the
evaluation phase. Upon joining the network, each vehicle receives the latest
model from the server, establishing a synchronized starting point for all
nodes. Subsequently, as nodes contribute their local models and share them
with the server, the model aggregation process is initiated, detailed in
Section 4.2.2. The server then facilitates the aggregation of local malicious
vehicles lists and furnishes the aggregated list to each vehicle in the cloud.
This serves as a pivotal element in the system’s overall functionality.

####  3.1.3 Communication Links

Communication links serve as a vital conduit for efficient data transfer, with
vehicles actively contributing to the central server’s repository of the
latest models. This bidirectional communication ensures that both the server
and vehicles maintain current and synchronized information.

Communication links, providing the connection to the server, incorporates
components that facilitate efficient data transfer. We anticipate leveraging
cellular networks for reliable data transmission from vehicles to the central
server. Alternatively, low Earth orbit satellite communications, or radio
technologies are available for connections between vehicles and the server.
The choice of the communication technology (links) depends on the specific
requirements and environmental conditions.

The potential inclusion of Road Side Units (RSUs) is an additional
consideration for enhancing communication, but the proposed system is designed
to operate seamlessly with or without them. One of the key strengths of the
proposed system is its adaptability to various communication links and
methods. It does not rely on any particular type of communication technology,
offering the flexibility to be applied in diverse scenarios. Given that the
proposed system does not involve the sharing of sensitive data that could be
reconstructed by attackers, conventional communication security methods are
sufficient for safeguarding the system.

####  3.1.4 Cold Start Phase

For the ’cold start’ phase the ADVENT system leverages a combination of
initializations for the central server and immediate model distribution for
new nodes joining the network. By incorporating these strategies, we ensure a
seamless integration process and maintain synchronization within the VANET
environment. The central server, residing in the cloud, undergoes a crucial
initialization process at the onset of the Cold Start. To bootstrap its
functionality, the server taps into the wealth of knowledge derived from
simulations or real-world data as carried out in our evaluations. These
initial simulations equip the server with fundamental insights into potential
attack scenarios, communication patterns, and network dynamics.

When a new node joins the network, a key challenge lies in swiftly integrating
it into the existing ecosystem. Our system addresses this challenge by
adopting an immediate model distribution approach. Upon successful
authentication with the central server, a new node promptly receives the
latest model, ensuring that it is synchronized with the current state of the
network. This immediate integration process allows the newcomer to actively
contribute to the collaborative detection efforts without delay.

By leveraging a combination of simulation-derived initialization for the
central server and immediate model distribution for new nodes, our system
guarantees a seamless integration process. This integration is not only
efficient but also critical for maintaining synchronization within the VANET
environment. The synchronized state serves as the linchpin for effective
collaboration among nodes and the central server.

The Cold Start phase lays a robust foundation for the system’s continuous
operation. It functions as a catalyst, enabling the system to adapt to varying
network conditions, detect malicious behavior in real-time, and respond
dynamically to changes within the VANET environment. This strategic
initialization approach ensures that the system is well-prepared to navigate
the intricacies of VANETs and provide reliable security solutions throughout
its operational lifecycle.

####  3.1.5 Data Processing

Vehicles actively process their received packets using our proposed methods.
Thus creating a continuous stream of data that they use in monitoring their
communications or creating new detection models to be shared with the server
their. This continuous operation forms the basis for ongoing model refinement
and adaptation to the evolving network conditions.

###  3.2 System’s Regular Operation

Each vehicle, upon joining the network, undergoes a standardized
initialization process thorugh receving the latest model from the central
server. The communication link facilitates the exchange of information,
allowing vehicles to actively contribute to the central server’s model
repository. Thus, under regular operation, when no attack is underway, the
components collaborate to monitor and secure the network.

Moreover, during regular operation, each vehicle processes its data stream
using the proposed system. Thus, each vehicle monitors its own observations,
which in return labels its own data based on what the vehicle observes.
Vehicles also train new attack onset detection models, ensuring that both the
server and individual vehicles maintain up-to-date models.

This synchronized operation under normal conditions enables the system to
proactively adapt to changing network dynamics, fostering a resilient and
efficient environment. The methodology prioritizes privacy by leveraging each
vehicle’s data for individual model training. Each node independently
constructs its model using local data without sharing this data with server,
reinforcing privacy measures within the system. The regular updates contribute
to the accuracy and relevance of the overall system.

####  3.2.1 Model Building: From the Perspective of the Server in the Cloud

In this system the XGBoost model is used, it belongs to the family of boosting
algorithms, which sequentially train a series of weak learners, typically
decision trees, to collectively form a robust predictive model [25].Our
approach utilizes this tree-based model to identify the onset of an attack. In
this paper, extend the approach based on the methodology presented in [10] by
leveraging FedXGBllr to simultaneously update our hyperparameters and enhance
privacy. The model is executed locally by each vehicle, resulting in the
creation of individual trees and weights. Subsequently, these vehicles
establish connections with the server to update the tree ensemble and weight
parameters for subsequent rounds. Once the server distributes the updated
trees, each vehicle becomes capable of detecting the onset of an attack. Upon
detection, the Malicious Node Detection component is activated to identify
potential attackers.

Under normal/regular operation, nodes actively participate in regular model
building activities, contributing to the collaborative effort of maintaining
an up-to-date model across the network. In this paper, four potential
approaches for model updates are proposed. The first approach invloves using
one model for a static interval, initiating the model preparation phase based
on the chosen interval. While this approach allows for flexibility in
adjusting the interval based on network conditions, it could cause repeating
the model preparation phase even when the network is stable. this could then
lead to unnecessary overhead.

The second approach involves counting the number of new nodes during the
authentication phase and setting a threshold. If the number of newly joined
nodes surpasses this threshold, the model preparation phase is triggered. This
method leverages the data from newly joined nodes for model building and
avoids unnecessary processes and overhead when the network is stable. However,
determining the appropriate threshold becomes crucial, as it needs to balance
the real network requirements.

The third approach involves renewing models based on the number of detected
attacks in the network. When a pre-determined threshold of attack onsets are
detected, the models are renewed, allowing nodes to use the updated models for
detecting subsequent attack onsets. While this ensures the system takes
advantage of all up-to-date data, determining the suitable threshold for the
number of attacks becomes essential to optimize the benefits.

The fourth approach proposes a comprehensive solution by combining different
strategies, creating a balanced and customized response to the network’s
specific requirements. In this approach, each incident is assigned a weight,
and a threshold is defined for the cumulative weight. If the combined weight
surpasses the established threshold, the system triggers the model renewal
process, ensuring the preparation of an up-to-date model. However, in the
absence of ongoing attacks and if no new vehicles meet the server’s new
arrival threshold, nodes can continue operating with the existing model,
avoiding unnecessary updates. This approach optimizes efficiency by activating
the model update process only when necessary, thereby aligning with the
system’s dynamic adaptation to varying network conditions.

###  3.3 System’s Operation under Attack

When a node detects the onset of an attack, it initiates the malicious node
detection component. This componenet generates a suspicious actor list based
on the data it observes and shares the list with the server. Upon receiving
the initial suspicious list, the attack operation phase commences at the
server. The first step in this phase involves determining the number of
vehicles reporting an attack onset, and enabling the server to assess whether
it might be a false positive. If the server confirms a real attack, it
proceeds to the next phase. In this phase, the server utilizes the previous
list of malicious nodes and the newly received suspicious nodes to prepare the
final list, which is then broadcasted to all connected nodes. Simultaneously,
the server considers the process of updating the model based on the new data
received, especially if this is the first attack onset detection after the
cold start process.

####  3.3.1  Malicious Node Detection

Upon detecting an attack onset, vehicles promptly activate the malicious node
detection module. Through communication with the server, multiple reports from
different vehicles are aggregated, enabling the server to validate and confirm
the attack. To prepare the final malicious list, the server considers all
suspicious lists received from nodes, implementing a threshold for the number
of reports for each suspicious node to prevent false positives. Subsequently,
the server aggregates and distributes the malicious node list to all nodes.
This collaborative approach empowers the network to collectively respond to
confirmed attacks, effectively minimizing the impact of false positives. The
distributed list ensures that every node remains informed about potential
threats, promoting a unified response to enhance overall network security.

In this step, two different approaches are considered for updating the
malicious list. The first approach is a stateless approach. This includes the
server creating the list each time and sending the final list to the nodes
without retaining it for future use. This approach is valuable considering the
highly changeable nature of VANETs. However, if an attacker remains silent for
a period and then resumes the attack, both the server and nodes would forget
about the previous state, necessitating a new detection process.

On the other hand, the second approach is a stateful approach. this includes
the server keeping the last list and setting a timer for each malicious node.
If the timer expires and there is no new report for the malicious node, the
server removes it from the next list. However, if the malicious node is
reported again, the server resets the timer and retains the node in the list.
This approach allows the system to maintain a memory of previous experiences,
avoiding the need to repeat the entire detection process for a previously
identified attacker. Nevertheless, keeping the timers up-to-date, setting
them, and deciding on the timer’s duration entail additional overhead for the
system.

Figure 1: Proposed system: Attack/Anomaly Detection in Vehicular NeTworks
(ADVENT)

##  4 Evaluations

In order to assess the performance and effectiveness of the proposed system,
we conducted a series of extensive experiments encompassing various attack
scenarios, which resulted in the creation of diverse datasets. Detailed
descriptions of these experiments are provided in the following subsections.

###  4.1 Simulated Dataset Creation

As previously mentioned, we utilized the Instant Veins5.1-i2 simulation
environment [26] to generate vehicular network data. Veins comprises OMNeT++
5.6.2 [27] and SUMO 1.8.0 [28], offering a comprehensive simulation suite. Our
simulations include four urban cities: Sao Paulo (Brazil), Manhattan (New
York, USA), Valencia (Spain), and Teruel (Spain).

We conducted six distinct simulation runs in each city, varying the percentage
of attackers from 5% to 30% in increments of 500 basis points. Each simulation
had a duration of 3600 seconds, equivalent to 24 real-world hours. Within this
timeframe, vehicles entered and exited the simulation after traversing
multiple edges on their routes to destinations. The number of edges each
vehicle traversed and the specific edges they crossed were determined by the
routes assigned within Sumo maps. The time intervals for adding vehicles were
adjusted based on the number of vehicles and the simulation’s duration. With
360 vehicles and a 3600-second simulation, the interval was set to 9.5 seconds
to allow sufficient time for the last vehicles to complete their trips. The
initial ten vehicles were introduced within the first ten seconds in all
scenarios, resulting in a range of 10 to 30 vehicles being present in the grid
during the simulations at any given time.

Throughout each simulation, we executed six attacks, each spaced 600 seconds
apart. Each attack had a duration of 25 simulation seconds, equivalent to 25
real-world minutes. When an attacker was present in the simulation, it engaged
in attacks against other vehicles for the entire 25-second period or for the
duration of its presence. At all other times, malicious nodes behaved normally
while within the simulation. We collected data from all vehicles during the
simulation, subsequently analyzing this data to evaluate the method’s
performance and efficiency under varying attack scenarios and geographical
conditions. For clarity and easy reference, the simulation configuration
details are presented in Table 2.

Table 2: Simulation Parameters Simulation Time (Seconds) | 3600
---|---
Route Scenario | Realistic urban
Mobility Model | SUMO
Propagation Model | Two-Ray Ground
Physical and MAC Layer | IEEE 802.11p
Routing Protocol | AODV
Transmission Rate | 6Mbps BW10MHz
Radio Coverage (Meters) | 400
Transmission Power (dBm) | 33.8 – 60.0
Transport Protocol | UDP
Total Vehicles | 360
Total Serving Vehicles | 10 - 30
Total Malicious Vehicles | 5% - 30%
Attack Duration Time (Seconds) | 25
Number Of Attacks | 6
Table 3: Summary of the Datasets Used Dataset | Total # of | # of benign | # of malicious
---|---|---|---
| Samples | Samples | Samples
Manhattan-5% | 4,676,165 | 4,039,228 | 636,937
Manhattan-10% | 5,083,855 | 3,995,742 | 1,088,113
Manhattan-15% | 5,794,656 | 3,908,557 | 1,886,099
Manhattan-20% | 6,253,454 | 3,856,242 | 2,397,212
Manhattan-25% | 6,622,686 | 3,856,103 | 2,766,583
Manhattan-30% | 7,248,367 | 3,735,463 | 3,512,904
Sao Paolo-5% | 12,986,664 | 7,054,112 | 5,932,552
Sao Paolo-10% | 1,336,3751 | 10,880,979 | 2,482,772
Sao Paolo-15% | 14,184,846 | 10,624,604 | 3,560,242
Sao Paolo-20% | 14,850,165 | 10,414,500 | 4,435,665
Sao Paolo-25% | 15,322,125 | 10,067,179 | 5,254,946
Sao Paolo-30% | 15,941,078 | 9,832,657 | 6,108,421
Teruel-5% | 4,579,237 | 3,602,391 | 976,846
Teruel-10% | 5,218,344 | 3,591,633 | 1,626,711
Teruel-15% | 5,881,409 | 3,526,322 | 2,355,087
Teruel-20% | 6,330,491 | 3,489,388 | 2,841,103
Teruel-25% | 7,098,750 | 3,604,679 | 3,494,071
Teruel-30% | 8,159,407 | 4,731,502 | 3,427,905
Valencia-5% | 3,757,466 | 2,995,307 | 762,159
Valencia-10% | 4,190,315 | 3,001,785 | 1,188,530
Valencia-15% | 4,765,503 | 2,961,888 | 1,803,615
Valencia-20% | 5,057,464 | 2,928,309 | 2,129,155
Valencia-25% | 5,665,209 | 2,952,889 | 2,712,320
Valencia-30% | 6,535,266 | 3,667,503 | 2,867,763

Following all the aforementioned steps, we generated 24 datasets, each
described in Table 3. This table provides details on the number of samples,
along with the counts of both normal and malicious samples in each dataset.
After completing the simulations, the next stage involves data preparation for
training and testing the proposed system. For each node in the simulation, we
aggregate the count of received packets from various nodes in 1-second
intervals. This data serves as the foundation for feature engineering,
focusing on the last 10 seconds of packet reception history, which results in
10 distinct features. These features encapsulate the packet counts received
from different senders over time, supplying valuable information for training
and evaluating tree-based models.

In essence, if there is an anomalous change in the number of packets received
from each node per second, this alteration is highly likely to be detected
within the initial second of observation. Our results substantiate this
notion, underscoring the effectiveness of early detection in the first second
of observation. This method introduces a novel approach to data preprocessing
and feature engineering, aiming to enhance detection rates, achieve
dimensionality reduction, and enhance accuracy while minimizing false alarms
and time complexity.

In contrast to traditional feature selection methods, which can introduce
additional overhead and complexity, our approach involves simplified and
expedited calculations. These calculations allow us to directly provide
results to the models, eliminating the need for extensive feature selection
procedures. Consequently, this approach offers a more efficient and
streamlined process for data preparation, enhancing model training without
compromising performance.

Given these considerations, a dataset is created for each vehicle, where each
dataset consists of n rows, corresponding to the aforementioned time periods.

  * •

Let line($t_{i}$) be the row created for time period $t_{i}$.

  * •

Let a be a randomly chosen integer, a represents the number of features for
the dataset. We set a = 10 in our work.

  * •

Then $\text{Line}(t_{i})$ contains
$\textit{Count}(t_{i}),\textit{Count}(t_{(i-1)}),\\\
\textit{Count}(t_{(i-2)}),\ldots,\textit{Count}(t_{(i-(a-1))})$ as the
features.

  * •

Each line is labelled as a positive exemplar if the attack is in progress at
time period $t_{i}$, negative otherwise.

Since we intend to perform both training and testing for each vehicle
separately, we need to segment the datasets based on each vehicle’s unique ID.
This segmentation allows us to run the method and obtain results for each
vehicle individually. In Table 4, we present the average time taken by each
node for data preprocessing and dataset preparation for training and testing.

Table 4: Time (in Seconds)  Dataset | Prepro. | Attack Onset De. | Malicious Node De.
---|---|---|---
Manhattan-5% | 0.70 | 5.46 | 0.004
Manhattan-10% | 0.70 | 6.2 | 0.004
Manhattan-15% | 0.70 | 6.26 | 0.004
Manhattan-20% | 0.71 | 5.85 | 0.005
Manhattan-25% | 0.71 | 5.78 | 0.005
Manhattan-30% | 0.72 | 5.86 | 0.005
Sao Paolo-5% | 0.72 | 6.26 | 0.008
Sao Paolo-10% | 0.72 | 5.96 | 0.008
Sao Paolo-15% | 0.71 | 5.88 | 0.008
Sao Paolo-20% | 0.72 | 5.59 | 0.008
Sao Paolo-25% | 0.73 | 5.41 | 0.008
Sao Paolo-30% | 0.74 | 5.65 | 0.009
Teruel-5% | 0.70 | 5.93 | 0.004
Teruel-10% | 0.70 | 6.62 | 0.005
Teruel-15% | 0.70 | 6.94 | 0.005
Teruel-20% | 0.70 | 5.85 | 0.005
Teruel-25% | 0.71 | 5.79 | 0.005
Teruel-30% | 0.71 | 5.86 | 0.006
Valencia-5% | 0.69 | 6.57 | 0.004
Valencia-10% | 0.69 | 6.51 | 0.004
Valencia-15% | 0.70 | 5.73 | 0.004
Valencia-20% | 0.70 | 5.69 | 0.004
Valencia-25% | 0.70 | 5.86 | 0.004
Valencia-30% | 0.71 | 6.46 | 0.005

Determining what constitutes ”enough” data for training is crucial for ML
algorithms. To this end, we employed the Synthetic Minority Over-sampling
Technique (SMOTE) to address this challenge by augmenting the dataset. The
goal is to ensure that the learning model is provided sufficient and
representative data, optimizing its training for improved performance. This
approach aligns with our overarching research objective of achieving robust
and effective model outcomes across various VANET scenarios.

###  4.2 The Proposed System: ADVENT

In our evaluation of our proposed system, ADVENT, the process starts with each
vehicle (node) training an XGBoost model using its local data. Subsequently,
the node sends the resulting trees, accompanied by a unique Client ID (CID),
to the central server. The server then consolidates these trees and, in the
following steps, updates the global model weights (W) to improve efficiency
while safeguarding the privacy of individual vehicle data. In the event of any
ongoing attacks during these rounds, ADVENT triggers the Malicious Node
Detection component. To identify potential threats, each vehicle calculates a
threshold based on the count of inbound connections from neighboring nodes,
labeling those vehicles exceeding this threshold as Suspected. The lists of
Suspected vehicles are then collectively shared with the server to create an
aggregated list, and the Malicious Node list is subsequently distributed to
all nodes for proactive attack prevention and protection against receiving
compromised data.

####  4.2.1 Data Preprocessing Component

Each node in the network receives packets from all neighbors, and two
different data presentations are required for the two main following steps in
detecting malicious behavior.

For attack onset detection, each node actively computes the number of received
packets from all nodes in the neighborhood within a predefined time interval,
creating a history of network behavior. This history is then used to detect
the onset of an attack. Each node actively and continously prepares this
history and utilizes it for attack onset detection.

The second data type needed is the number of received packets from each node
and the Mean Absolute Deviation (MAD) calculated based on these numbers. Each
node computes MAD based on the received packets from each node in intervals of
1 minute until it detects the onset of an attack. Once an attack onset is
detected, nodes run the procedure in 10-second intervals to identify and block
malicious nodes. Afterward, they revert to 1-minute intervals until another
attack onset is detected. The intervals can be adjusted based on network
conditions to achieve optimal efficiency.

####  4.2.2 Attack Onset Detection Component

In Figure 2, the process of attack onset detection commences with the
acquisition of the preprocessed data, as elaborated in 4.2.1, where the number
of received inbound connections are computer over 1-second windows over the
last 10 seconds. Subsequently, the XGBoost classifier is built using these
data.

Figure 2: The Attack Onset Detection system

At time t=0 (time 0 seconds), all nodes train their local XGBoost tree
ensemble and send the trees to the server. The server aggregates the tree
ensemble and initializes a Convolutional Neural Network (CNN). After receiving
the aggregated tree ensemble, nodes calculate prediction outcomes given the
aggregated trees on their local data samples, and these outcomes become inputs
for the CNN.

Notably, clients only build XGBoost models at round 0, and the aggregated tree
ensemble remains fixed after round 0. For federated training of the one-layer
1D CNN after round 1, the protocol follows a standard FL algorithm, updating
weights for 9 rounds, finalizing the model each node can use during its
travel. Results of our simulations indicate that it takes around 6 seconds
before nodes can start detecting the onset of an attack.

####  4.2.3 Malicious Node Detection Component

Figure 3 presents the overview of the Malicious Node Detection component,
which becomes active when the Attack Onset Detection component detects an
initiation of an attack. This component’s first step is to get the number of
inbound connections from each node. Subsequently, it moves on to establishing
the threshold for identifying anomalous behavior, a process performed based on
the Median Absolute Deviation (MAD) algorithm. [5, 13, 15]

Figure 3: The Malicious Node Detection system

MAD employs an anomaly detection technique, specifically leveraging Median
Absolute Deviation as a statistical model for detecting outliers or anomalies.
MAD computes the median of the data and considers the absolute differences
between each data point and the median. Through MAD, the system scrutinizes
the number of connection requests received from neighboring vehicles, seeking
indications of anomalies like flooding attacks or DDoS attacks perpetrated by
malicious vehicles.

Advent calculates MAD using Equation (1). It should be noted here that the
median of each series is calculated and multiplied by ’b = 1.4826’ which
ensures the estimator’s consistency with respect to the parameter of interest
[29].

$MAD=bM_{i}(|x_{i}-M_{j}(x_{j})|)$ (1)

By utilizing MAD, ADVENT identifies anomalies in the number of requests,
abnormal behaviors in vehicular networks, contributing to the detection of
DDoS attacks.

The next critical step involves establishing a rejection threshold, denoted as
tr. Values exceeding this threshold are classified as outliers or anomalies.
To determine ’tr’ and evaluate different exclusion criteria (ce),based on
prior work [5] the value of ce = 3.0 is set. Equation (2) is used for
calculating the rejection threshold:

$M-ce*MAD<t_{r}<M+ce*MAD$ (2)

where tr represents the rejection threshold, either negative or positive, used
to identify outliers. MAD represents the calculated Median Absolute Deviation
value. ce represents the exclusion criterion, and M represents the median
value of the original classified series.

Once the rejection threshold tr is determined based on the chosen exclusion
criterion, it is applied to the anomaly detection process. Data points falling
outside of this threshold are classified as outliers or anomalies [15].
Determining the threshold enables the detection mechanism to distinguish
between normal and abnormal behavior based on data dispersion. This
facilitates the identification of potential malicious nodes or unusual
patterns within the VANET environment.

Up to this point, we have introduced a method for detecting malicious nodes.
However, there is a crucial concern, as discussed by Baharlouei et al. in
[13]. Using this method, the results are heavily influenced by the chosen grid
pattern and each node’s experience of encountering attackers. Specifically, if
a vehicle is located far from an attacker or if there are obstructions that
hinder packet reception, that vehicle may not receive sufficient packets from
the attacker to identify it as malicious. Consequently, it might not include
the attacker in its suspicious list. This could result in a high FNR and
affect the overall F1-score as well as DR.

To address this challenge, ADVENT utilizes Federated Learning, which leverages
each node’s unique experience and allow vehicles to share these experiences
with one another. This collaborative approach ensures that all vehicles
possess the most accurate and comprehensive blacklist of potential malicious
nodes, enabling them to interact with their neighbors more effectively.

####  4.2.4 Federated Learning Component

Federated Learning (FL) is an innovative approach to collaborative machine
learning, gaining recognition for its role in preserving data privacy and
optimizing data-driven applications, in particular within VANETs[30]. FL
differs from traditional centralized approaches by enabling model training on
local devices, such as vehicles in VANETs, allowing them to learn from their
data while aiming to ensure data privacy[31]. In FL, only the model updates
are shared among the nodes, eliminating the need to exchange raw data to
preserve data privacy. This decentralized approach offers multiple advantages
for VANETs, including enhanced security, reduced communication overhead, and
increased adaptability[32]. Furthermore, FL promotes the development of robust
and accurate models by aggregating diverse local knowledge, empowering
vehicles to collaboratively identify malicious nodes, predict traffic
patterns, and enhance the overall performance of a vehicular network [32]

Indeed, FL can play a crucial role in improving the FNR within the Malicious
Node Detection system of ADVENT. By employing FL to aggregate lists and
harness the collective experience of various nodes, a comprehensive overview
of the network and the behavior of all vehicles can be aggregated. This
collaborative approach allows us to detect malicious nodes, reduce the
likelihood of false negatives, and improve the accuracy of the detection
process.

#### Federated Learning in Attack Onset Detection

In this step instead of transmitting raw data from each vehicle for
processing, we opt for sharing models and trees during the training process,
aligning with the principles of FL. Our research leverages FedXGBllr [10],
which deviates from existing methods of federated XGBoost. Rather than relying
on gradient sharing, the FedXGBllr approach takes a novel route by minimizing
per-node level communication frequency and mitigating privacy concerns. This
system goes beyond privacy enhancement and also optimizes communication
efficiency. It achieves this by enabling the learning rates of the aggregated
tree ensembles to be adjusted, ensuring the security of sensitive information
during model training optimization. [10].

Figure 4 provides a visual representation of this component of the system,
illustrating how the server receives the trees and Client IDs (CIDs) from the
vehicles. The process commences with the server’s aggregation and broadcast of
the trees and associated parameters. This methodology ensures the creation of
a reliable and robust model without imposing excessive processing demands on
the vehicles or the need to share extensive data among vehicles. Next, we will
delve into a detailed discussion of the Federated Learning method we employed.

Figure 4: The Federated Learning-Attack Onset Detection system

####  In the Initial Round (t = 0)

Upon receiving the trained XGBoost tree ensembles and Client IDs (CIDs) from
vehicles that have trained their local XGBoost trees using available data, the
server undertakes the aggregation of these tree ensembles. This critical step
is instrumental in facilitating the training of a global federated XGBoost
model. The server proceeds by:

  * •

Sorting and broadcasting the aggregated XGBoost tree ensemble along with
corresponding CIDs to all clients.

  * •

Initializing w0 for the one-layer 1D CNN.

  * •

Each client receives the broadcasted aggregated XGBoost tree ensemble and
proceeds to evaluate these ensembles with their localized dataset. This
evaluation yields prediction outcomes generated by all trees for each data
sample.

####  For Subsequent Rounds(R) (t = 1 to R)

  * •

The server designates the clients who will participate.

  * •

Global model parameters, represented as wt, are broadcast to all clients by
the server.

  * •

In parallel fashion, each client updates its local model parameters to wk(t+1)
by performing a Client Update operation, utilizing the received parameters.

  * •

Subsequently, the server aggregates the updated models from all clients to
calculate the new global model parameters, wk(t+1).

This iterative process unfolds over subsequent rounds, ensuring that the
global model continually refines itself with each step. This sophisticated and
collaborative approach aligns seamlessly with the principles of Federated
Learning, promoting effective global model improvement while keeping
individual data private. It also offers the advantage of faster training with
local data, removing the need for servers in the training process [12].

#### Federated Learning in Malicious Node Detection

Fig. 5 illustrates the application of the Federated Learning approach to the
detection of malicious nodes. In previous work by Baharlouei et al. as
described in [13], the MDASTI method was utilized for malicious node
detection, and it highlighted a significant issue concerning the False
Negative Rate (FNR). The variation in each vehicle’s experiences within a
network depends largely on factors such as the number of attackers and the
grid pattern, as well as geographical characteristics of the simulations.
Since these factors are not controllable and can introduce considerable
variability into the results, it becomes imperative to take measures to
mitigate these side effects. To address this, we propose a novel method that
aims to combine the collective experience of all nodes, allowing vehicles to
view the network from the perspective of other vehicles. After the Malicious
Node Detection component has been executed in a vehicle and list of suspicious
actors is generated, this new component is called into action. In this
component, as depicted in Fig. 5, the server receives the suspicious lists
from all vehicles and proceeds with the following steps:

  * •

Count the frequency of each reported vehicle i, denoted as Fi

  * •

If the frequency of a reported vehicle, Fi, is higher than the predefined
threshold ”TH” set for the network, then include that vehicle in the list of
malicious nodes

  * •

Transmit the malicious node list to all vehicles in the network

Figure 5: The Federated Learning-Malicious Node Detection system

This method aids nodes in identifying malicious nodes within the network even
before they receive a substantial number of inbound connections or come under
attack from malicious nodes. It significantly contributes to the reduction of
the False Negative Rate (FNR), allowing nodes to base their decisions on the
maximum connection requests sent by each node in the network rather than the
proportion they receive. The inclusion of this component in the system has led
to several positive achievements: Firstly, a notable reduction in FNR, coupled
with an increase in DR and F1-score, as previously discussed in our prior
work. [13]; secondly, nodes are now capable of predicting attacks before the
influx of inbound connections, effectively avoiding interactions with
malicious nodes; thirdly, it has minimized the impact of differing
geographical patterns and varying numbers of attackers on the network.

###  4.3 Performance Metrics

In our study, we employed the standard evaluation metrics, namely Detection
Rate (DR), False Alarm Rate (FAR), False Negative Rate (FNR), and F1-score,
which are commonly used to assess the performance of a machine learning model.
These metrics were used to evaluate the systems accuracy in the context of
attack onset detection and malicious node identification. These metrics are
defined as follows: Detection Rate (DR) measures the proportion of true
positive instances correctly identified, False Alarm Rate (FAR) quantifies the
proportion of false alarms, False Negative Rate (FNR) evaluates the rate of
false negatives, and the F1-score is a balanced measure of precision (positive
predictive value) and recall (Sensitivity or True Positive Rate). To calculate
these metrics, we used the following formulas, where True Positive (TP)
represents the number of correctly identified positive instances, True
Negative (TN) denotes the number of correctly identified negative instances,
False Negative (FN) indicates the number of instances mistakenly classified as
negative, and False Positive (FP) signifies the number of instances mistakenly
classified as positive. These metrics are instrumental in evaluating the
effectiveness of our proposed system.

$DR=TP/(TP+FN)$ (3)

$FAR=FP/(FP+TN)$ (4)

$FNR=FN/(TP+FN)\\\ $ (5)

$Recall=TP/(TP+FN)\\\ $ (6)

$Precision=TP/(TP+FP)\\\ $ (7)

$\textit{F1-score}=2*(Precision*Recall)/(Precision+Recall)$ (8)

###  4.4 Results

In ADVENT, once each vehicle has trained its local model and shared the trees
with the server, the server aggregates these trees and distributes them to all
vehicles during round 0. Subsequently, only the model weights are updated,
following the methodology outlined in [10]. To maintain consistency with this
reference in our simulations, we have set the number of rounds to 10 and
Epochs to 100, with a batch size of 64. The XGBoost parameters have been
configured based on the default values specified in [10]. After training each
vehicle’s model and optimizing it, we conducted testing using the unseen data
for each vehicle. The results are presented in the following figures.

#### Results of Attack Onset Detection

: Attack Onset detection harnesses XGBoost to identify the initiation of
attacks. We began our analysis by considering the DR, as depicted in Figure 6,
we compared the outcomes of employing Federated Learning as a distributed
method to uphold privacy, where nodes operate and train locally based on their
data. Contrasting these results with the centralized approach, as seen in the
figure, where XGBoost is trained with 70% of each dataset and tested with the
remaining 30%, ADVENT achieved more realistic outcomes. This approach
considers the variation in results across datasets, reflecting the
predictability of obtaining diverse outcomes in datasets with varying numbers
of malicious and benign samples. Over 20 datasets, it is observed the results
remain consistent in most scenarios except in some cases where the performance
decreases slightly. The disparity is more evident in smaller datasets where
there are fewer samples to train local models for both benign and malicious
instances. Sao Paolo exhibited the best results, having the largest dataset,
followed by Teruel, Manhattan, and Valencia with the smallest dataset,
resulting in the least favorable outcomes. Furthermore, as the number of
attackers increase in each city, consequently augmenting the number of attack
onset samples, we observe that results are improved, leading to a reduction in
differences among datasets.

Figure 6: Attack Onset Detection-Detection Rate

Moving on to the next metric, F1_Score, calculated using equation 8, we
observe a pattern similar to the DR results. Sao Paolo consistently
demonstrates the best performance due to its larger dataset, allowing for
ample samples to train local models in each node. Additionally, across all
nodes, a higher number of attackers results in a higher F1_Score, indicating
improved performance as more samples are available for training. However, it’s
noteworthy that with 55 attackers, the F1_Score does not yield the optimal
results.

Figure 7: Attack Onset Detection-F1-Score

In Figure 8, the discernible surge in the FNR from an average of 0.02% to
2.48% can be directly attributed to a lower number of samples available for
training the model. This effect is particularly pronounced in the Manhattan
and Valencia scenarios, where the datasets are smaller, especially when
dealing with 5% of attackers. The reduced sample size in these instances
contributes to the deterioration of FNR results, highlighting the critical
role of dataset size in influencing model performance especially when we try
to train the model locally and provide privacy.

Figure 8: Attack Onset Detection-False Negative Rate

In Figure 9, a marginal shift in the FPR is evident, moving from 0% to 0.11%.
This subtle change underscores the reliability of the model in accurately
detecting the attack, emphasizing its trustworthiness even under these
comparative conditions.

Figure 9: Attack Onset Detection-False Positive Rate

The results seen so far seem to suggest that the number of samples per node
significantly influences both the training process and the quality of the
produced trees. We, thus, delved deeper to explore the relationship between
dataset size and detection ability. Table 5 outlines the number of malicious
and normal samples in both the train and test datasets after dataset
preparation and preprocessing. The last column provides the Normal/Malicious
ratio, revealing that Sao Paolo attains the best results with the lowest
ratio. Further analysis in Table 3 indicates that Sao Paolo also possesses the
largest datasets. Additionally, the ratio varies with changes in the number of
attackers, being lower in scenarios with a higher number of attackers.

Table 5: # of Malicious and Normal samples in Datasets after preprocessing the datasets DataSet | Malicious | Normal | Train
---|---|---|---
Test | Train | Test | Train | Normal/Mal
Manhattan-5% | 518 | 1418 | 394042 | 914658 | 645.03
Manhattan-10% | 700 | 1586 | 393860 | 914254 | 576.45
Manhattan-15% | 745 | 1682 | 393815 | 914158 | 543.49
Manhattan-20% | 762 | 1719 | 393798 | 914121 | 531.77
Manhattan-25% | 785 | 1774 | 393775 | 914066 | 515.26
Manhattan-30% | 810 | 1830 | 393750 | 914010 | 499.46
Sao Paolo-5% | 1331 | 3035 | 384589 | 892645 | 294.12
Sao Paolo-10% | 1319 | 2980 | 384601 | 892700 | 299.56
Sao Paolo-15% | 1327 | 2997 | 384593 | 892683 | 297.86
Sao Paolo-20% | 1345 | 3035 | 384575 | 892645 | 294.12
Sao Paolo-25% | 1354 | 3053 | 384566 | 892627 | 292.38
Sao Paolo-30% | 1364 | 1364 | 384556 | 384556 | 281.93
Teruel-5% | 646 | 1456 | 390674 | 906824 | 622.82
Teruel-10% | 738 | 1670 | 390582 | 906610 | 542.88
Teruel-15% | 783 | 1776 | 390537 | 906504 | 510.42
Teruel-20% | 807 | 1818 | 390513 | 906462 | 498.60
Teruel-25% | 849 | 1913 | 390471 | 906367 | 473.79
Teruel-30% | 905 | 2038 | 390415 | 906242 | 444.67
Valencia-5% | 426 | 1305 | 389814 | 904793 | 693.33
Valencia-10% | 672 | 1536 | 389568 | 904224 | 588.69
Valencia-15% | 709 | 1607 | 389531 | 904153 | 562.63
Valencia-20% | 728 | 1647 | 389512 | 904113 | 548.95
Valencia-25% | 769 | 1738 | 389471 | 904022 | 520.15
Valencia-30% | 818 | 1849 | 389422 | 903911 | 488.86

Further explorations are conducted in order to understand the relationship
between the Normal/Malicious data ratio and the DR. As illustrated in Figure
10,there seems to be a direct correlation between the Normal/Malicious sample
ratio in each dataset and the DR. Notably, Sao Paolo, with the lowest ratio,
yielded the most favorable results, while Valencia, with the highest ratio,
exhibited the poorest outcomes. To mitigate this imbalance and enhance local
model training, we integrate Synthetic Minority Over-sampling Technique
(SMOTE) into ADVENT. SMoTE is utilized to ensure that the ratio of
Normal/Malicious samples is 2.9% across all datasets. In doing so, our aim is
to evaluate the impact on the results and move closer to the performance
achieved through centralized training.

Figure 10: Attack Onset Detection-False Positive Rate

Going forward, our analysis includes three approaches: Centralised Training,
Federated Learning, and integrating Federated Learning with SMOTE. We present
our analysis and discussions for each approach over six datasets for each
city. It should be noted here that the following abbreviations are used for
different scenarios in the figures hereafter: Centralized Training (CT-5),
using Federated Learning (FL-5), and implementing Federated Learning with
SMOTE (FLS-5) on datasets with 5% of attackers, and so forth.

In Figure 11b, the DR for Sao Paolo remained consistent as we applied the
threshold based on this city, with no change in the number of training
samples. However, Figure 11a illustrates a significant impact on DR in
Manhattan after applying SMOTE and increasing the number of malicious training
samples. While the average DR was 99.96% with Centralized Training and 97.33%
with the Federated Learning approach, it rose back to 99.78% with the use of
SMOTE. As shown in Figure 11c, the DR was 99.98% with Centralised Training,
reduced to 98.58% with Federated Learning, and then increased to 99.61% with
the application of SMOTE. Figure 11d depicts the results for Valencia, where
the DR showed the most significant difference—decreasing from 99.96% to 96.47%
and rebounding to 99.57% after balancing the data using SMOTE with the
identified thresholds.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 11: FLS-Attack Onset Detection-Detection Rate

In Figure 12a through 12d, we present the F1-Score for Sao Paolo, Manhattan,
Teruel, and Valencia, respectively. A consistent pattern emerges across these
figures, with 100% F1-Score achieved through Centralized Training, dropping to
98% with Federated Learning (decentralized training), and rebounding to 99.67%
with the application of SMOTE in Manhattan. The results for Sao Paolo follow a
similar trend. In Teruel, we started with a 100% F1-Score using XGBoost and
training with 70% of each dataset, shifted to 98.03% with Federated Learning
and local training, and further increased to 99.81% with SMOTE. The most
notable increase is observed in Valencia, where we traded 100% F1-Score for
enhanced privacy, resulting in a 97.02% F1-Score with local training. By
applying the threshold and SMOTE, we recovered to 99.78% F1-Score in Valencia.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 12: FLS-Attack Onset Detection-F1-Score

In Figures 13a to 13d, we observe a solitary case of the FPR in Valencia
datasets, where the value is just under 1%. In all other cases, the FPR
remains at 0 or under 0.01%, indicating exceptionally good performance. The
consistently low FPRs across different scenarios and datasets emphasize the
robustness and efficacy of ADVENT in minimizing false positives.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 13: FLS-Attack Onset Detection-F1-Score

While the FPR consistently yielded favorable results across different
scenarios tested on various datasets, challenges surfaced in the FNR. The
scarcity of malicious samples during the local training phase significantly
impacts the FNR, consequently influencing the DR and F1-Score. However,
implementing a threshold, defined based on results from diverse datasets and
cities, leads to notable improvements.

In Manhattan datasets, where the CT achieved a 0% FNR, local training resulted
in a 2.67% FNR, which further decreased to 0.22% with the application of
SMOTE. In Sao Paolo, which served as the baseline for threshold determination,
no substantial change was observed. Teruel exhibited fewer changes compared to
Manhattan and Valencia, moving from a 0.03% FNR to 1.42% during local
training, and then reverting to 0.39%. Valencia, following a similar pattern
to previous results, witnessed significant fluctuations. Starting with a 0.04%
FNR in our baseline method, it increased to 3.53% with FedXGBllr and decreased
to 0.43% with the application of SMOTE. The results are shonw in Figures 14a
to 14d.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 14: FLS-Attack Onset Detection-F1-Score

The final factor we explore is how quickly attacks are detected to provide
insight into the effectiveness of ADVENT in preventing further harm to
vehicles and the network in realtime. Figure 15 illustrates the results,
indicating an average of 99.6% of all attacks being detected within the first
second of commencement. This showcases the high efficacy of our method in
swiftly identifying and responding to attacks as they unfold, contributing to
the overall security and resilience of the vehicular ad-hoc network.

Figure 15: Valencia-FLS-Attack Onset Detection-FPR

#### Results of Malicious Node Detection

: Malicious Node Detection involves the utilization of the Median Absolute
Deviation (MAD) technique. Subsequently, nodes share the lists of suspicious
vehicles with a central server, enabling the server to compile a finalized
list of malicious vehicles, which is then distributed to all vehicles.
Following these steps, we observed significant improvements, particularly in
terms of DR and FNR. These enhancements were particularly noteworthy when
compared to our previous work, as discussed in [13], where we encountered a
higher FNR due to variations in vehicle experience and the packets received.
It’s worth noting that on the server side, ADVENT utilizes a frequency-based
threshold to mitigate the FPR. This threshold enables ADVENT to reduce the
impact of false reports. We conducted an experiment with a threshold value set
to 2 in this study to evaluate its effect compared to not applying the
threshold. It’s essential to mention that the threshold value can be adjusted
based on the specific network conditions and features, allowing for
adaptability to different scenarios.

Through the utilization of this vehicle-specific testing methodology and the
computation of average metrics, we obtain valuable insights into the
performance of the proposed method on individual vehicles. In Fig 16, we
present the DR results for the three compared methods. The first method, as
outlined in [5], applies the MAD technique on the same 24 datasets and
computes the average results from all nodes. The second method involves using
Federated Learning with the sharing of suspected vehicle lists with the server
to obtain the malicious node list. The third method explores the impact of
setting a threshold value to 2, designating a vehicle as an attacker if it is
reported by two or more vehicles.

Notably, the application of the Federated Learning method yields a remarkable
improvement in the DR, reaching 100% for all cities. This marks an average
increase of 17.45% for Valencia, 18.46% for Teruel, 18.10% for Manhattan, and
17.39% for datasets pertaining to Sao Paulo. However, the utilization of the
threshold does lead to a decrease in the average DR for Manhattan and Sao
Paulo, dropping it to 98.55%. On the other hand, Teruel and Valencia maintain
a 100% DR even after the application of the threshold.16a to 16d Additionally,
a pivotal achievement of this method is the notable reduction in disparities
among results from various cities, especially when we use pure federated
learning aggregation method, where all the cities acheive 100% DR. This shows
the effectiveness of integrating these steps and methodologies in mitigating
the influence of geographical patterns and attackers’ distribution, both of
which were discussed in previous work [5, 13]. The significance of this
achievement is rooted in the understanding that geographical variations can
profoundly impact the efficacy of each method, and the proposed approach
successfully addresses these challenges.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 16: Malicious Node Detection-Detection Rate

The next metric under discussion is the F1 score, and Figures 17a to 17d
provide a detailed breakdown of the results across all 24 datasets. On
average, for Sao Paulo, the F1 score increases from 88.56% with the MAD
techniques to 93% with the Federated Learning and further improves to 96.03%
with the application of the threshold. Similarly, Manhattan, which initially
averages an F1 score of 89.25% with MAD, reaches 92.43% when using Federated
Learning and aggregating the lists, ultimately achieving an impressive 97.88%
F1 score with the threshold.

In the Teruel datasets, even better results are obtained, starting at 89.57%
F1 score and moving to 97.43% for the Federated Learning and ultimately
reaching 100% with the integration of the threshold. Valencia exhibits a
similar pattern to Sao Paulo and Manhattan, with the F1 score increasing from
88.97% to 93.50%, reflecting a 4.53% improvement via Federated Learning, and
then a 97.50% F1 score with an additional 4% improvement, upon applying the
threshold.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 17: Malicious Node Detection-F1-Score

In Figures 18a to 18a, the results for the FNR are shown, and the outcomes are
quite remarkable. We observed a substantial decrease in the FNR for all
cities, namely Sao Paulo, Manhattan, Teruel, and Valencia. The FNR reduced
from 18.62%, 18.10%, 18.34%, and 17.45%, respectively, to around 0% when
applying Federated Learning, benefiting from the collective wisdom of all
nodes and aggregating their experiences. Furthermore, after setting the
threshold to 2, we still experienced some FNR in Sao Paulo and Manhattan,
averaging at 1.42% and 1.45%, respectively. However, it is important to note
that in the other two cities, Teruel and Valencia, the FNR remained at 0.
Nonetheless, when compared to the previous work in [13], this study
demonstrates a significant improvement, with a meaningful reduction in FNR.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 18: Malicious Node Detection-False Negative Rate

In Fig. 19, we present the results for the FPR, highlighting the effects of
incorporating a threshold and combining it with the Federated Learning method.
This approach strikes a balance between FPR and FNR. Initially, with the basic
MAD technique, the FPR was very low, measuring at 0.00%, 0.14%, 0.03%, and
0.33% for Sao Paulo, Manhattan, Teruel, and Valencia, respectively. However,
when we apply the Federated Learning method, there is an increase in FPR to
1.23%, 1.04%, 0.35%, and 1.12% for the same cities. While these FPR values are
not very high, applying the threshold helps reduce them to an average of 0.68%
for Sao Paulo, 0.25% for Manhattan, 0.00% for Teruel, and 0.41% for Valencia,
resulting in a favorable balance for all four cities.

(a) Manhattan

(b) Sao Paolo

(c) Teruel

(d) Valencia

Figure 19: Malicious Node Detection-False Positive Rate

###  4.5 Time complexity

Table 4 provides statistical insights into the system’s real-world
performance, presenting the preprocessing time for each dataset in the first
column, the time taken to detect the attack onset in the second column, and
the duration ADVENT system takes to identify the list of malicious nodes in
the third column. According to the statistics in this figure, ADVENT achieves
synchronization and generates a unified model for network use within an
average of 6 seconds. Moreover, it demonstrates the capability to detect the
onset of an attack in the very first second in 99.6% of the scenarios and
identifies malicious behavior in 1.97 seconds in average.

##  5 Conclusions and Future Works

In conclusion, our exploration into the realm of VANETs has uncovered both the
potential and the challenges associated with this dynamic and critical
communication infrastructure. While VANETs offer real-time solutions for
addressing delayed driver responses and enhancing traffic safety, their
intrinsic vulnerabilities, such as the ever-changing network topologies and
the diverse nature of V2X communications, demand a dedicated focus on security
[1].

To bolster VANET security, we introduce an innovative 3-step real-time
malicious behavior detectorcalled ADVENT. ADVENT seamlessly integrates machine
learning and statistical methodologies to ensure the effective detection of
attack onsets. The results consistently showcase an impressive average
detection rate of 99.6% for DDoS attack onsets occurring in the first second.
It also excels in identifying malicious nodes, a crucial aspect for
maintaining the integrity of the network, achieving a remarkable 99.28%
Detection Rate (DR). Importantly, we prioritize data privacy through the
implementation of Federated Learning, avoiding the transmission of raw data.
This not only enhances efficiency but also minimizes the False Negative Rate
(FNR), resulting in a substantial reduction from 18.13% to 0.72% FNR, when
compared to prior work [5, 13].

Our pioneering preprocessing method stands out for its effectiveness in
understanding communication patterns and potential attack scenarios. This
method significantly reduces dataset size and processing time, contributing to
a more efficient exploration of network dynamics.

Addressing the challenge of imbalanced data, we identify a threshold and
implement the Synthetic Minority Over-sampling Technique (SMOTE) to balance
datasets. This yields improved outcomes and minimizes the FNR, showcasing a
notable improvement in the DR from 97.95% to 99.59% on average and a reduction
in the FNR from 2.05% to 0.41% on average. These contributions collectively
establish a comprehensive multi-layered real-time detection method, privacy-
enhanced Federated Learning, and an efficient feature selection process.

The robust evaluation of our proposed method consistently demonstrates its
remarkable effectiveness, achieving an overall Detection Rate (DR) of 99.66%.
Leveraging Federated Learning significantly reduces the FNR, thereby enhancing
overall model performance. The feature selection process contributes to the
efficiency of attack detection models. Realistic VANET simulations ensure the
reliability of our datasets and the presented results. Furthermore, the
implementation of SMOTE effectively balances imbalanced data, leading to
improved results and minimized FNR. These contributions collectively reinforce
the security and reliability of VANETs, marking a significant step forward in
addressing the evolving challenges in vehicular communication networks.

For future work, an essential avenue involves expanding the system’s
applicability by testing it on diverse datasets. This includes datasets with
varying characteristics and scenarios to assess the system’s adaptability and
generalization capabilities. Additionally, exploring the system’s performance
against different types of attacks beyond DDoS will provide valuable insights
into its robustness and effectiveness in a broader range of security threats.
By systematically evaluating the system’s response to various datasets and
attack scenarios, we can enhance its versatility and establish a more
comprehensive understanding of its capabilities in real-world environments.
Furthermore, there is potential for refining the model-building aspect. The
discussed different approaches could be further explored, discussed, and
tested to identify the most effective approach. Additionally, there is room to
work on optimizing communication links and exploring various choices to
enhance the overall efficiency and reliability of the system. These future
directions will contribute to the continual improvement and advancement of the
proposed system in tackling evolving challenges in vehicular communication
networks.

## Acknowledgment

This research is made possible through the support of the Mitacs funding
program. The research is conducted at the Dalhousie Network and Information
Management System (NIMS) Lab and the Cybersecurity Lab at New York Institute
of Technology in Vancouver, Canada.

## References

  * [1] M. T. Garip, M. E. Gursoy, P. Reiher, and M. Gerla, “Congestion attacks to autonomous cars using vehicular botnets,” _Proceedings 2015 Workshop on Security of Emerging Networking Technologies_ , 2015.
  * [2] F. A. Ghaleb, M. Aizaini Maarof, A. Zainal, B. A. S. Al-Rimy, F. Saeed, and T. Al-Hadhrami, “Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network,” _IEEE Access_ , vol. 7, pp. 159 119–159 140, 2019.
  * [3] F. A Ghaleb, F. Saeed, M. Al-Sarem, B. Ali Saleh Al-rimy, W. Boulila, A. Eljialy, K. Aloufi, and M. Alazab, “Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for vanet,” _Electronics_ , vol. 9, no. 9, p. 1411, 2020.
  * [4] F. J. Martinez, C. K. Toh, J.-C. Cano, C. T. Calafate, and P. Manzoni, “A survey and comparative study of simulators for vehicular ad hoc networks (vanets),” _Wireless Communications and Mobile Computing_ , vol. 11, no. 7, pp. 813–828, 2011.
  * [5] H. Baharlouei, A. Makanju, and N. Zincir-Heywood, “Exploring realistic vanet simulations for anomaly detection of ddos attacks,” in _2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)_ , 2022, pp. 1–7.
  * [6] F. J. Martinez, M. Fogue, C.-K. Toh, J.-C. Cano, C. T. Calafate, and P. Manzoni, “Computer simulations of vanets using realistic city topologies,” _Wireless Personal Communications_ , vol. 69, no. 2, pp. 639–663, 2013.
  * [7] H. Hasrouny, A. E. Samhat, C. Bassil, and A. Laouiti, “Vanet security challenges and solutions: A survey,” _Vehicular Communications_ , vol. 7, pp. 7–20, 2017.
  * [8] F. Sakiz and S. Sen, “A survey of attacks and detection mechanisms on intelligent transportation systems: Vanets and iov,” _Ad Hoc Networks_ , vol. 61, pp. 33–50, 2017.
  * [9] D. V. de Queiroz, J. C. M. Vieira, and I. E. Fonseca, “Detecçao de ataques de negaçao de servico utilizando ferramentas de monitoramento e análise de tráfego,” _Revista de Tecnologia da Informação e Comunicação_ , vol. 4, no. 1, pp. 28–35, 2014.
  * [10] C. Ma, X. Qiu, D. Beutel, and N. Lane, “Gradient-less federated gradient boosting tree with learnable learning rates,” in _Proceedings of the 3rd Workshop on Machine Learning and Systems_ , ser. EuroMLSys ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 56–63. [Online]. Available: https://doi.org/10.1145/3578356.3592579
  * [11] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: Synthetic minority over-sampling technique,” _Journal of artificial intelligence research_ , vol. 16, pp. 321–357, 2002.
  * [12] L. Yang, A. Moubayed, and A. Shami, “Mth-ids: A multitiered hybrid intrusion detection system for internet of vehicles,” _IEEE Internet of Things Journal_ , vol. 9, no. 1, pp. 616–632, 2022.
  * [13] H. Baharlouei, A. Makanju, and N. Zincir-Heywood, “Exploring real-time malicious behaviour detection in vanets,” in _Proceedings of the Int’l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications_ , ser. DIVANet ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 1–8. [Online]. Available: https://doi.org/10.1145/3616392.3623412
  * [14] E. R. Cavalcanti, J. A. R. de Souza, M. A. Spohn, R. C. d. M. Gomes, and A. F. B. F. d. Costa, “Vanets’ research over the past decade: Overview, credibility, and trends,” _SIGCOMM Comput. Commun. Rev._ , vol. 48, no. 2, p. 31–39, may 2018. [Online]. Available: https://doi.org/10.1145/3213232.3213237
  * [15] E. Pastori Valentini, R. Ipolito Meneguette, and A. Alsuhaim, “An attacks detection mechanism for intelligent transport system,” in _2020 IEEE International Conference on Big Data (Big Data)_ , 2020, pp. 2453–2461.
  * [16] L. Yang, A. Moubayed, I. Hamieh, and A. Shami, “Tree-based intelligent intrusion detection system in internet of vehicles,” in _2019 IEEE Global Communications Conference (GLOBECOM)_. IEEE Press, 2019, p. 1–6. [Online]. Available: https://doi.org/10.1109/GLOBECOM38437.2019.9013892
  * [17] P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” _Computers and Electrical Engineering_ , vol. 105, p. 108543, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0045790622007583
  * [18] N. Abd, K. M. Ali Alheeti, S. Salah, and S. Al-Rawi, “Intelligent intrusion detection system in internal communication systems for driverless cars,” _Webology_ , vol. 17, p. 376, 12 2020.
  * [19] Y. Gao, H. Wu, B. Song, Y. Jin, X. Luo, and X. Zeng, “A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network,” _IEEE Access_ , vol. 7, pp. 154 560–154 571, 2019.
  * [20] M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Multi-stage optimized machine learning framework for network intrusion detection,” _IEEE Transactions on Network and Service Management_ , vol. 18, no. 2, pp. 1803–1816, jun 2021. [Online]. Available: https://doi.org/10.1109%2Ftnsm.2020.3014929
  * [21] L. Yang, A. Moubayed, I. Hamieh, and A. Shami, “Tree-based intelligent intrusion detection system in internet of vehicles,” in _2019 IEEE Global Communications Conference (GLOBECOM)_ , 2019, pp. 1–6.
  * [22] E. Seo, H. M. Song, and H. K. Kim, “GIDS: GAN based intrusion detection system for in-vehicle network,” in _2018 16th Annual Conference on Privacy, Security and Trust (PST)_. IEEE, aug 2018. [Online]. Available: https://doi.org/10.1109%2Fpst.2018.8514157
  * [23] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in _International Conference on Information Systems Security and Privacy_ , 2018.
  * [24] K. Cheng, T. Fan, Y. Jin, Y. Liu, T. Chen, D. Papadopoulos, and Q. Yang, “Secureboost: A lossless federated learning framework,” _IEEE Intelligent Systems_ , vol. 36, no. 06, pp. 87–98, nov 2021.
  * [25] Y. Xia, C. Liu, Y. Li, and N. Liu, “A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring,” _Expert Systems with Applications_ , vol. 78, pp. 225–241, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417417301008
  * [26] C. Sommer, R. German, and F. Dressler, “Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis,” _IEEE Transactions on Mobile Computing (TMC)_ , vol. 10, no. 1, pp. 3–15, January 2011.
  * [27] A. Varga, _OMNeT++_. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 35–59. [Online]. Available: https://doi.org/10.1007/978-3-642-12331-3_3
  * [28] P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in _The 21st IEEE International Conference on Intelligent Transportation Systems_. IEEE, 2018. [Online]. Available: https://elib.dlr.de/124092/
  * [29] C. Leys, C. Ley, O. Klein, P. Bernard, and L. Licata, “Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median,” _Journal of experimental social psychology_ , vol. 49, no. 4, pp. 764–766, 2013.
  * [30] Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Federated learning for data privacy preservation in vehicular cyber-physical systems,” _IEEE Network_ , vol. 34, no. 3, pp. 50–56, 2020.
  * [31] T. Alam and R. Gupta, “Federated learning and its role in the privacy preservation of iot devices,” _Future Internet_ , vol. 14, no. 9, p. 246, 2022.
  * [32] M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad, “Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives,” _Electronics_ , vol. 12, no. 10, 2023. [Online]. Available: https://www.mdpi.com/2079-9292/12/10/2287