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Found 7 papers, 2 papers with code
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.
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.
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection
In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files?
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection
This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints.
Automatic Labeling for Entity Extraction in Cyber Security
Timely analysis of cyber-security information necessitates automated information extraction from unstructured text.
PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts
Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases.
