Abstract
Numerous security researchers have a growing interest in the vulnerabilities of the in-vehicle Controller Area Network (CAN) bus system to cyber-attacks. The adversaries can leverage these vulnerabilities in manipulating vehicle functions and harming the drivers’ safety. Some security mechanisms proposed for CAN bus in detecting anomalies have favoured over the one-class classification, where it constructs a decision boundary from normal instances. Nevertheless, the accuracy performance of the classifier is highly influenced by the data representation. Judging from this fact, this paper analyses the advantage of utilizing different feature scaling technique as in to obtain higher classification accuracy of the classifier algorithms. To serve this purpose, the CAN bus datasets in this paper are scaled using standardization, min-max, and quantile, and are evaluated using one-class classifier model used in automotive CAN bus. The results exhibit that integrating different feature scaling techniques could greatly enhance the classification accuracy of the classifiers.
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Lokman, SF., Othman, A.T., Bakar, M.H.A., Musa, S. (2020). The Impact of Different Feature Scaling Methods on Intrusion Detection for in-Vehicle Controller Area Network (CAN). In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2019. Communications in Computer and Information Science, vol 1132. Springer, Singapore. https://doi.org/10.1007/978-981-15-2693-0_14
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