A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm

Recently, with the growth of electronic cars and the advancement of modern vehicles using portable equipment and embedded systems, several in-vehicle networks like the CAN (Controller Area Network) encountered novel risks of security. Because the portal of CAN does not have systems of security, like encryption and authentication in order to contend with cyber-attacks, the necessity for a system of intrusion detection for identifying attacks on the portal of CAN is really essential. In this study, Triple-attention Mechanism (TAN) has been used to recognize different kinds of security intrusions in portals of CAN. The purpose of TAN here is to identify intrusion within 3 steps. Within the initial phase, the major features have been extracted, and TAN functions as a descriptor of feature. Then, the discriminating categorizer classifies the current features. Eventually, with the help of adversarial learning, intrusion has been recognized. The current work utilizes a novel Greylag Goose Optimization algorithm for optimal selection of the network hyperparameters. For checking the effectiveness of the suggested method, an open-source dataset was applied, which recorded the traffic of CAN using a real vehicle throughout injection attacks of message. The results show that this method outperforms certain machine learning algorithms in error rate and false negative for DoS and drive gear and RPM spoofing attack with accuracy of 96.3%, recall of 96.1%, F1-Score of 96.2%, specificity of 97.2%, accuracy of 96.3%, AUC-ROC of 0.97, and MCC of 0.92 for DoS attacks. Therefore, the phase attack is minimized.