AN INTELLIGENT DATA DRIVEN MODEL TO SECURE INTRA VEHICLE COMMUNICATIONS BASED ON MACHINE LEARNING
Keywords:
Electric Vehicles, Cyber-Attack, Anomaly Detection, Support Vector Machine (SVM), Controller Area Network (CAN) Protocol, Social Spider Optimization (SSO) Algorithm, Malicious Attack Detection, Denial-of-Service (DoS) Hacking, Security Framework, Simulation Results.Abstract
The high relying of electric vehicles on either in vehicle or between-vehicle communications can
cause big issues in the system. This paper is going to mainly address the cyber-attack in electric vehicles and
propose a secured and reliable intelligent framework to avoid hackers from penetration into the vehicles. The
proposed model is constructed based on an improved support vector machine model for anomaly detection
based on the controller area network (CAN) bus protocol. In order to improve the capabilities of the model for
fast malicious attack detection and avoidance, a new optimization algorithm based on social spider (SSO)
algorithm is developed which will reinforce the training process offline. Also, a two-stage modification method
is proposed to increase the search ability of the algorithm and avoid premature convergence. Last but not least,
the simulation results on the real data sets reveal the high performance, reliability and security of the proposed
model against denialof-service (DoS) hacking in the electric vehicle
