ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7
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Abstract
Description
Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises
and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion
detection systems using different algorithms are proposed to enhance the security of the in-vehicle
network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge"
during 2020. This dataset has been realized by the organizers of the challenge for security researchers.
With the aid of this dataset, the work aimed to develop attack and detection techniques of Controller Area
Network (CAN) using different algorithms such as support vector machine and Feedforward Neural
Network. This research work also provides a comparison of the rendering of these algorithms. Based on
experimental results, this work will help future researchers to benchmark their results for the given
dataset. The results obtained in this work show that the model selection does not depend only on the
model's accuracy that is explained by the accuracy paradox. Therefore, for the overall result accuracy of
62.65%, they show that the support vector machine presents the most satisfying output in terms of
precision and recall. The Radial basis kernel gives 65% and 67% precision for fuzzing and flooding and
the recall of 64% and 100% for replay and spoofing, respectively.
Keywords
QA75 Electronic computers. Computer science