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http://dx.doi.org/10.15207/JKCS.2018.9.6.033

Detection of Car Hacking Using One Class Classifier  

Seo, Jae-Hyun (Division of Computer Science & Engineering, WonKwang University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.6, 2018 , pp. 33-38 More about this Journal
Abstract
In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.
Keywords
vehicle; hacking; intrusion detection; one class classification; unsupervised learning; machine learning;
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Times Cited By KSCI : 5  (Citation Analysis)
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