In this paper, we propose H-IDFS, a Histogram-based Intrusion Detection and Filtering framework, which assembles the CAN packets into windows, and computes their corresponding histograms. The latter are fed to a multi-class IDS classifier to identify the class of the traffic windows. If the window is found malicious, the filtering system is invoked to filter out the normal CAN packets from each malicious window. To this end, we propose a novel one-class SVM, named OCSVM-attack that is trained on normal traffic and considers the invariant and quasi-invariant features of the attack. Experimental results on two CAN datasets: OTIDS and Car-Hacking, show the superiority of the proposed H-IDFS, as it achieves an accuracy of 100% for window classification, and correctly filters out between 94.93% and 100% of normal packets from malicious windows.


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    Title :

    Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks




    Publication date :

    2022-03-01


    Size :

    2747462 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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