Attacks detection and decision framework based on generative adversarial network approach: Case of vehicular edge computing network

The application of artificial intelligence in cyber security context has attracted enormous attention, specifically from industrial sector and this is due to the strength of machine learning algorithms to detect the unknown misbehaviors executed by cyber-attacks. The generative adversarial network (GAN) based on a generator and discriminator systems could be used as defense and attack frameworks to protect and attack respectively its target. In this research article, we propose new attacks detection and decision framework based on GAN algorithm to detect accurately the smart and dangerous attacks. The proposed security framework relies on the collaboration between the generator and discriminator systems to determine the relevant attacks' features and hence detect the cyber-attacks with a high accuracy. We present a case study of the proposed detection and decision framework in a context of a vehicular edge computing network and highlight the experimental results by analyzing specifically, the accuracy defense rate and network latency. In the experimentation results, a maximum number of network attacks are detected with a high accuracy, while low network latency is generated. This result is achieved specifically when the number of iterations is high, that is, the accuracy defense rate is close to 90% and the network latency is close to 1000 milliseconds.