An Advanced Wasserstein-Enabled Generative Adversarial Network Enabled Attack Detection for Blockchain-Assisted Intelligent Transportation System

In day-to-day life, intelligent transport system (ITS) is playing an indispensable role in enhancing mobility and minimizing the carbon track. The security and enduring maintenance of the ITS from cyberattacks are essential. To achieve highly enhanced protection over attacks, a new blockchain (BC) technology with a deep learning (DL) model is introduced to secure the ITS system. Initially, the signature is created and verified by BC blocks, and hash values are chained to resist various attacks that try to falsify sensitive data along with the associated transactions. Moreover, the Advanced Wasserstein Generative Adversarial Network (Advanced-WGAN) technique is introduced to detect various distributed attacks that pause the immediate and confidential data of the shareholders. The simulation progression is carried out on the Python platform and the openly accessible CICDDoS2019 database is considered for the training process. The outcomes prove that the developed method attains an accuracy of 99%, F-score (FS) of 98.5%, precision of 99%, and recall of 99.2%. The experimental results show that the developed approach is highly secure against vulnerable cyberattacks.