1 Assistant Professor of Department of CSE(AI&ML).
2 Students of Department CSE (AI&ML) of ACE Engineering College.
World Journal of Advanced Research and Reviews, 2025, 25(01), 1992-2000
Article DOI: 10.30574/wjarr.2025.25.1.0283
Received on 17 December 2024; revised on 25 January 2025; accepted on 21 January 2025
This study addresses cyber-attacks in Electric Vehicles (EVs) and proposes an intelligent, secure framework to protect both in-vehicle and vehicle-to-vehicle communication systems. The proposed model uses an improved support vector machine (SVM) for anomaly and intrusion detection based on the Controller Area Network (CAN) protocol a critical component in vehicle communication. To further enhance detection speed and accuracy a new optimization algorithm the Social Spider Optimization (SSO) is introduced for reinforcing the offline training process. Simulation results on real-world datasets demonstrate the model's high performance, reliability and ability to defend against denial-of-service (DoS) attacks in EVs.
Cyber-Attacks; Electric Vehicles (Evs); Intelligent Framework; Controller Area Network (CAN); Anomaly Detection; Intrusion Detection; Support Vector Machine (SVM); Social Spider Optimization (SSO); Offline Training, Simulation Results; Denial-Of-Service (Dos) Attacks
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Chitoor Venkat Rao Ajay Kumar, Parnam Venkatagirish, Sai Srinivas Patibandla and Kapil Rathod. Real Time Anomaly Detection and Intrusion Detection for Safeguarding Intra-Vehicle Communication Powered by AI. World Journal of Advanced Research and Reviews, 2025, 25(01), 1992-2000. Article DOI: https://doi.org/10.30574/wjarr.2025.25.1.0283
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