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Öğe Detection of Cyber Attacks Targeting Autonomous Vehicles Using Machine Learning(Springer Science and Business Media Deutschland GmbH, 2024) Onur, Furkan; Barışkan, Mehmet Ali; Gönen, Serkan; Kubat, Cemallettin; Tunay, Mustafa; Yılmaz, Ercan NurcanThe advent of Industry 4.0, characterized by the integration of digital technology into mechanical and electronic sectors, has led to the development of autonomous vehicles as a notable innovation. Despite their advanced driver assistance systems, these vehicles present potential security vulnerabilities, rendering them susceptible to cyberattacks. To address this, the study emphasized investigating these attack methodologies, underlining the need for robust safeguarding strategies for autonomous vehicles. Existing preventive or detection mechanisms encompass intrusion detection systems for Controller Area Networks and Vehicle-to-Vehicle communication, coupled with AI-driven attack identification. The critical role of artificial intelligence, specifically machine learning and deep learning subdomains, was emphasized, given their ability to dissect vehicular communications for attack detection. In this study, a mini autonomous vehicle served as the test environment, where the network was initially scanned, followed by the execution of Man-in-the-Middle, Deauthentication, DDoS, and Replay attacks. Network traffic was logged across all stages, enabling a comprehensive analysis of the attack impacts. Utilizing these recorded network packets, an AI system was trained to develop an attack detection mechanism. The resultant AI model was tested by transmitting new network packets, and its detection efficiency was subsequently evaluated. The study confirmed successful identification of the attacks, signifying the effectiveness of the AI-based model. Though the focus remained on autonomous vehicles, the study proposes that the derived methodology can be extended to other IoT systems, adhering to the steps delineated herein. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Öğe Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems(Pergamon-Elsevier Science Ltd, 2024) Onur, Furkan; Gonen, Serkan; Bariskan, Mehmet Ali; Kubat, Cemallettin; Tunay, Mustafa; Yilmaz, Ercan NurcanWith the advancement of humanity, transportation and trade activities have increased, leading to the development process of basic land vehicles as more than physical power became necessary. Hand tools were developed with the invention of the wheel, followed by animal-powered vehicles, and then steam engine technology. After the advancement of electromechanical technologies, today's modern vehicles have been developed. Those who used these vehicles thought of transferring control from the human to autonomous driving systems to solve their safety and comfort problems. Today, instead of fully autonomous systems targeted for the future, autonomous driving support systems have been developed. Although these systems aim to increase the safety and comfort of passengers, they can become an easy target for malicious people due to network technologies and remote connection features. The most effective method of protection from these attackers is to conduct vulnerability analysis against newly emerging threats for the systems we use and to rectify identified vulnerabilities. In this research paper, the weaknesses of wireless communication towards remote connection usage of the mini electric autonomous vehicle were investigated, which we developed and produced its mechanics, electronics, and software. In this context, a test environment was created, and the problems and threats in autonomous driving technology were revealed through attacks (Deauth Attack, DoS, DDoS and MitM) made on the test environment. Following the exposed vulnerabilities, studies were conducted for the detection of these attacks using Artificial Intelligence. In the study, different algorithms were used to detect the attacks, and random forest algorithm successfully detected 96.1% of attacks. The main contribution to the field of cybersecurity in autonomous vehicles by providing effective solutions for threat identification and defense.