Optimized Attention Induced Multi Head Convolutional Neural Network for Intrusion Detection Systems in Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is enhancing comfort and traffic control and have brought about a paradigm shift in the design of contemporary transportation systems. However, as smart sensing technologies become more widely used with the advent of the Internet of Things (IoT), intruders have found vehicular sensor networks to be a soft target. In this article, an optimized Attention Induced Multi Head Convolutional Neural Network for Intrusion Detection System in VANETs (AIMHCNN-IDS-VANET) is proposed. The data is collected from the CAN_HCRL_OTIDS dataset. This data is fed to a pre-processing segment where Tanh-based normalization (ThN) is used to normalize the data. Then, the pre-processed data serves as input to AIMHCNN which classifies the data into denial of service (DoS) attack, fuzzy attack, impersonation attack, and normal (attack-free). In general, AIMHCNN doesn’t express some adaption of optimization approaches to determine optimal parameters to assure accurate classification of attack detection. Hence, the Capuchin search optimization algorithm is proposed to enhance the weight parameter of the AIMHCNN classifier, which precisely classifies the IDS. The proposed method is implemented and its efficacy is analyzed on several performance parameters. The method is observed to attain higher accuracy, higher precision, and higher specificity when compared with existing methods.