A Machine Learning Based Approach to Detect Cyber-Attacks on Connected and Autonomous Vehicles (CAVs)

Nazaruddin, Safwan Abdul; Chaudhry, Umair B.

doi:10.1007/978-3-031-33631-7_6

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…The author of [17] focuses on developing machine learning models using multiple techniques and evaluating them using specific criteria to find and suggest the best-suited model for detecting attacks in CAVs. Furthermore, other terminologies associated with CAVs are defined in this study, including CAV, CAV architecture, CAV cyber security, and various hazards and vulnerabilities inherent in the CAN bus.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed detector creates a trajectory subspace using an offline incremental-learning-based approach and can successfully identify stealthy attacks. [17] Focuses on developing machine learning models using multiple techniques and evaluating them to find and suggest the best-suited model for detecting attacks in CAVs. The study also defines various terminologies associated with CAVs and discusses potential attacks and mitigation strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

Kim,

Krichen,

Alamro

et al. 2024

Electronics

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Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

Kim,

Krichen,

Alamro

et al. 2024

Electronics

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Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment

Ali,

Imran,

Ullah

et al. 2024

Sensors

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Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

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A Machine Learning Based Approach to Detect Cyber-Attacks on Connected and Autonomous Vehicles (CAVs)

Cited by 2 publications

References 45 publications

A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment

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