An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for In-Vehicle Network

dc.contributor.advisorMahgoub, Imadeldin
dc.contributor.authorAlalwany, Easa
dc.date.accessioned2023-11-14T07:49:57Z
dc.date.available2023-11-14T07:49:57Z
dc.date.issued2023-11-13
dc.descriptionmy PhD dissertation
dc.description.abstractConnectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this dissertation, to address all these concerns, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers, to detect normal and abnormal activities in the CAN bus. Moreover, we design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle's driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. Ensemble learning aims to achieve better classification results through the use of different classifiers that are combined into a single classifier. Furthermore, in the pursuit of real-time attack detection and classification, we propose IDS scheme that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa architecture. The Kappa architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. We build this system using the most recent CAN intrusion dataset provided by the IEEE DataPort. We carried out the performance evaluation of the proposed system in terms of accuracy, precision, recall, F1-score, and area under curve receiver operator characteristic (ROC-AUC). For the binary classification, the ensemble classifiers outperformed the individual supervised ML classifiers and improved the effectiveness of the classifier. For detecting and classifying CAN bus attacks, the ensemble learning methods resulted in a robust and accurate multi-classification IDS for common CAN bus attacks. The stacking ensemble method outperformed other recently proposed methods, achieving the highest performance. For the real-time attack detection and classification, the ensemble methods significantly enhance the accuracy of real-time CAN bus attack detection and classification. By combining the strengths of multiple models, the stacking ensemble technique outperformed individual supervised models and other ensembles.
dc.format.extent102
dc.identifier.urihttps://hdl.handle.net/20.500.14154/69684
dc.language.isoen_US
dc.publisherSaudi Digital Library
dc.subjectMachine learning
dc.subjectSecurity
dc.subjectIn-vehicle networks
dc.titleAn Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for In-Vehicle Network
dc.typeThesis
sdl.degree.departmentElectrical Engineering and Computer Science
sdl.degree.disciplineMachine Learning, Security, In-vehicle Networks
sdl.degree.grantorFlorida Atlantic University
sdl.degree.nameDoctor of Philosophy
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