Increasing Cyber-Security in Independent Vehicles through the Integration of Verification Vector Machines and Defense Multiplicative Adversarial Networks

Abstract

Autonomous vehicles (AVs) are a significant advancement in transportation technology, offering greater safety, performance and comfort. Traditional cyber-security solutions frequently fail to defend antivirus software from sophisticated assaults that exploit software and communication network flaws. This study proposes using Support Vector Machine Fused Defense Generative Adversarial-Network (SVM-DGAN) to improve cyber-security in autonomous vehicles. In cyber-security, SVMs can be employed for threat intelligence by analyzing data to identify patterns and classify data into different categories, such as normal behaviour or anomalous activities indicating potential cyber threats.  Defense GANs are employed for adaptive defense mechanisms to enhance the resilience of machine learning models against adversarial attacks in autonomous vehicle cyber-security. Initially we gather dataset from controller area networks (CAN). Pre-process the gathered data using minimum-maximum normalization. The following elements were used to compare the traditional and proposed methods in terms of accuracy (98.50%), precision (98%), recall (98.25%), and F1-score (97%). It shows that our proposed method is effective in autonomous vehicle cyber-security. The findings of our study affirm the viability and effectiveness of the SVM-DGAN framework as a formidable defence against cyber threats targeting autonomous vehicles