Cluster-based attacks prevention algorithm for autonomous vehicles using machine learning algorithms. (July 2022)

• Record Type: Journal Article
• Authors: Sankaranarayanan, R.; Umadevi, K.S.; Bhavani, NPG; Jos, Bos Mathew; Haldorai, Anandakumar; Babu, D. Vijendra
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: CNN model -- Machine learning -- Demand-side management -- Smart grid -- Computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108088
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Autonomous vehicles are the latest engineering research sensations. The development of these needs the collaboration of studies in the automotive and artificial intelligence domains. Self-driving cars face a slew of security issues when left on their own without human supervision. However, the massive volumes of data required for vehicle operation can't be processed rationally by humans. This is where modern intelligence systems can help. They can facilitate clear decision-making regarding the state of the systems. A unique technique based on Machine Learning (ML) procedures is proposed in this paper. Two cluster models have been constructed and coupled with three Convolutional Neural Networks (CNN) models based on Demand-Side Management (DSM). These models are integrated to determine the optimal operating time, stability, and management model. Data from the previous three years have been used in this study. The results show that the proposed CNN and cluster 2 models are the best for energy optimization. The best thing is that this real-time model can meet the demands of large traffic. Graphical abstract: Image, graphical abstract