Detection of Cyber Attacks Targeting Autonomous Vehicles Using Machine Learning

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dc.authorscopusid58652302600
dc.authorscopusid57447588400
dc.authorscopusid57201743399
dc.authorscopusid6603658187
dc.authorscopusid56798444400
dc.authorscopusid56469924100
dc.contributor.authorOnur, Furkan
dc.contributor.authorBarışkan, Mehmet Ali
dc.contributor.authorGönen, Serkan
dc.contributor.authorKubat, Cemallettin
dc.contributor.authorTunay, Mustafa
dc.contributor.authorYılmaz, Ercan Nurcan
dc.date.accessioned2024-09-11T19:58:23Z
dc.date.available2024-09-11T19:58:23Z
dc.date.issued2024
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023 -- 26 May 2023 through 28 May 2023 -- Istanbul -- 302369en_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.1007/978-981-99-6062-0_40
dc.identifier.endpage449en_US
dc.identifier.isbn978-981996061-3en_US
dc.identifier.issn2195-4356en_US
dc.identifier.scopus2-s2.0-85174637507en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage439en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-99-6062-0_40
dc.identifier.urihttps://hdl.handle.net/11363/8460
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Mechanical Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectCyber Security; IIoT; IoT; Machine Learningen_US
dc.titleDetection of Cyber Attacks Targeting Autonomous Vehicles Using Machine Learningen_US
dc.typeConference Objecten_US

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