Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models

Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models

Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is the b...

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Bibliographic Details
Main Authors: Mark J. Potvin, Sylvain P. Leblanc
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Sensors
Subjects:
controller area network (CAN)
J1939
anomaly detection
long short-term memory
deep learning
automotive security
Online Access:https://www.mdpi.com/1424-8220/25/14/4430
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