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

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 i...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-03, Vol.23 (3), p.2366-2379
Main Authors: Derhab, Abdelouahid, Belaoued, Mohamed, Mohiuddin, Irfan, Kurniawan, Fajri, Khan, Muhammad Khurram
Format: Article
Language:English
Subjects:
Computer Science
Cryptography and Security
Distributed, Parallel, and Cluster Computing
Feature extraction
Filtering
filtering system
Filtration
histogram
Histograms
In-vehicle network
Intrusion
Intrusion detection
intrusion detection system
Invariants
Machine Learning
multi-class
Multiagent Systems
Networking and Internet Architecture
OCSVM
Safety
security
Vehicle-to-everything
Wireless fidelity
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3088998