Bağlantılı ve otonom araçlarda siber güvenlik çalışması

The rapid development of autonomous vehicle technologies has made the security requirements of these systems more complex and has made it necessary to take security measures against cybersecurity threats in in-vehicle communication networks. In this thesis, a literature review is presented that comprehensively examines academic and industrial studies in the field of cybersecurity for autonomous and connected vehicles. The main purpose of the study is to address anomaly detection methods developed to detect security vulnerabilities in autonomous vehicles and to comprehensively present academic studies evaluating the role of these methods in determining security vulnerabilities. The differences between academic and industrial approaches in the field of cybersecurity are discussed in detail and an overview is provided regarding developments in the fields of generative artificial intelligence, machine learning, artificial intelligence and anomaly detection. In addition, various types of attacks such as fuzzy, malfunction and flooding, which are widely covered in the literature on cybersecurity vulnerabilities in autonomous vehicles and carry potential risks, are discussed and the prevention methods used against such attacks are evaluated. In the continuation of the study, various machine learning algorithms are divided into different test percentages and run on the relevant data set and presented in tables. These analyses were conducted using the In-Vehicle Intrusion Detection System Dataset, and a detailed assessment is provided on effective measures that can be taken against cyber-attacks. This study aims to make a comprehensive contribution to cyber security research in the field of autonomous and connected vehicles, and to constitute an important reference in the literature on the detection of security vulnerabilities in autonomous vehicles.