Enhancing Vehicle Security: Intrusion Detection Using Machine Learning

Varghese, Anusha (2024) Enhancing Vehicle Security: Intrusion Detection Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Today’s Connected vehicles open completely new dimensions in the risk of cyberattacks due to the integration of complex electronic control units and communication within the vehicle. This paper presents several different machine learning-based methods for enhancement of accuracy and detection efficiency for an intrusion detection system for improved vehicular security. First, in-vehicle network data was collected and pre-processed, and critical features were selected for intrusion detection. The ensemble learning methods employed, including Random Forest and Voting Classifier, enhanced detection accuracy and adaptability. The main challenges addressed in this paper were high false-positive rates, computational overhead, and real-time processing in designing a scalable and adaptable IDS architecture. The proposed system was designed to be effective for different vehicle models and types of cyberattacks, enhancing security and reliability in modern vehicles.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Uncontrolled Keywords:	Intrusion Detection; Vehicle Security; Machine Learning; Cybersecurity; In-Vehicle Networks; Random Forest; Real-time Detection; Ensemble Learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	28 Jul 2025 15:03
Last Modified:	28 Jul 2025 15:03
URI:	https://norma.ncirl.ie/id/eprint/8275

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