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Found 6 papers, 1 papers with code
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them.
Detecting CAN Masquerade Attacks with Signal Clustering Similarity
Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle's CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks.
Graph-Based Machine Learning Improves Just-in-Time Defect Prediction
By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction.
Time-Based CAN Intrusion Detection Benchmark
Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs).
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset
Current public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, which lack fidelity.
Using Bursty Announcements for Detecting BGP Routing Anomalies
Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination.
