An Evolutionary Deep Learning-Based Anomaly Detection Model for Securing Vehicles
Abdollah Kavousi‐Fard, Morteza Dabbaghjamanesh, Tao Jin, Wencong Su, Mahmoud Roustaei
Abstract
This article proposes a deep learning based approach for cyber attack detection in the vehicles. The proposed method is constructed based on generative adversarial network (GAN) classification to assess the message frames transferring between the electric control unit (ECU) and other hardware in the vehicle. To this end, two networks called generator (G) and discriminator (D) will run an adversarial game to fool each other. In such a process, the most optimal structure is found which distinguish between the model normal behavior and abnormalities. Due to the instabilities existing in the GAN model, a new optimization method based on firefly algorithm is proposed to create a class of generators in a feasible region, i.e. the discriminator D. A three-stage modification method is also devised to increase the algorithm population diversity and reduce the possibility of falling in local optima. The performance of the model is assessed on the experimental dataset recorded from the OBD-II port of an undefined vehicle.