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http://dx.doi.org/10.7471/ikeee.2022.26.4.614

RIDS: Random Forest-Based Intrusion Detection System for In-Vehicle Network  

Daegi, Lee (Soongsil University)
Changseon, Han (Soongsil University)
Seongsoo, Lee (Soongsil University)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 614-621 More about this Journal
Abstract
This paper proposes RIDS (Random Forest-Based Intrusion Detection), which is an intrusion detection system to detect hacking attack based on random forest. RIDS detects three typical attacks i.e. DoS (Denial of service) attack, fuzzing attack, and spoofing attack. It detects hacking attack based on four parameters, i.e. time interval between data frames, its deviation, Hamming distance between payloads, and its diviation. RIDS was designed in memory-centric architecture and node information is stored in memories. It was designed in scalable architecture where DoS attack, fuzzing attack, and spoofing attack can be all detected by adjusting number and depth of trees. Simulation results show that RIDS has 0.9835 accuracy and 0.9545 F1 score and it can detect three attack types effectively.
Keywords
Controller Area Network; Intrusion Detection System; Random Forest; Machine Learning; Hacking; Automotive Cybersecurity; In-Vehicle Network;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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