Creating a Secure Autonomous Vehicle System Using a Neuro-Fuzzy System that Merges Artificial Neural Networks and Fuzzy Interface Systems

Abstract

This paper provides an overview of the potential use of Neuro-Fuzzy Systems (NFS) in safeguarding autonomous vehicles (AVs) against cyber-attacks. As innovative technology continues to permeate various aspects of daily life, the integration of advanced technologies, such as Artificial Neural Networks (ANN) and fuzzy inference systems (FIS), holds promise for enhancing the security of intelligent transport systems. With the increasing prominence of autonomous vehicles and self-driving cars in smart city systems, it is imperative to address vulnerabilities that may compromise their security. Existing vulnerabilities, including insecure applications and data-gathering vulnerabilities, pose significant obstacles to the widespread adoption of this technology. The potential consequences of security breaches in autonomous vehicles, such as endangering the lives of individuals both inside and outside of the vehicle, underscore the critical need for comprehensive security measures. By leveraging NFS, this study explored the feasibility of mitigating cyber-attacks targeting AVs, thereby bolstering their security and resilience against malicious intrusions.