Research of CAN Bus Information Anomaly Detection Based on Convolutional Neural Network
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This article is published in International Journal of Computer Theory and Engineering.The article was published on 2021-01-01 and is currently open access. It has received 0 citations till now. The article focuses on the topics: Convolutional neural network & Anomaly detection.read more
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Proceedings ArticleDOI
Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network
TL;DR: This paper proposes a light-weight intrusion detection algorithm for in-vehicle network based on the analysis of time intervals of CAN messages, and finds the time interval is a meaningful feature to detect attacks in the CAN traffic.
Proceedings ArticleDOI
Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks
TL;DR: An anomaly detector based on a Long Short-Term Memory neural network to detect CAN bus attacks is proposed and it is shown that the detector can detect anomalies the authors synthesized with low false alarm rates.
Journal ArticleDOI
A Survey of Intrusion Detection for In-Vehicle Networks
TL;DR: An IVN environment is introduced, and the constraints and characteristics of an intrusion detection system (IDS) design for IVNs are presented, and a survey of the proposed IDS designs for the IVNs is conducted.
Proceedings ArticleDOI
Cyber-Security for the Controller Area Network (CAN) Communication Protocol
TL;DR: A security mechanism to help prevent cyber-attacks in vehicles with architecture based on Controller Area Network, which keeps the bus utilization as low as possible and can achieve high security levels while keeping communication overheads at reasonable levels.
Proceedings ArticleDOI
A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security
Min-Ju Kang,Je-Won Kang +1 more
TL;DR: The proposed technique monitors an exchanging packet in the vehicular network while the feature are trained off-line, and provides a real-time response to the attack with a significantly high detection ratio in the authors' experiments.
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