mlsanityData to 14 Oct 2026

STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning

arXiv 2311.07056 ↗First posted 13 Nov 2023cs.NI cs.AI cs.CRcited by 0

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Abstract (arXiv)

In-vehicle network (IVN) is facing complex external cyber-attacks, especially the emerging masquerade attacks with extremely high difficulty of detection while serious damaging effects. In this paper, we propose the STATGRAPH, which is an effective and fine-grained intrusion detection methodology for IVN security services via multi-view statistical graph learning on in-vehicle controller area network (CAN) messages with insight into their variations in periodicity, payload and signal combinations. Specifically, STATGRAPH generates two statistical graphs, timing correlation graph (TCG) and coupling relationship graph (CRG), in every CAN message detection window, where edge attributes in TCGs represent temporal correlation between different message IDs while edge attributes in CRGs denote the neighbour relationship and contextual similarity. Besides, a lightweight shallow layered graph convolution network is trained based on graph property of TCGs and CRGs, which learns the universal laws of various patterns more effectively and further enhance the performance of detection. To address the problem of insufficient attack types in previous intrusion detection, we select two real in-vehicle CAN datasets covering five new instances of sophisticated and stealthy masquerade attacks that are never investigated before. Experimental result shows STATGRAPH improves both detection granularity and detection performance over state-of-the-art intrusion detection methods. Code is available at https://github.com/wangkai-tech23/StatGraph.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 13 Nov 2023.

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14 of 42 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2019Searching for MobileNetV3cited by 1,994
  4. 2019GIDS: GAN based Intrusion Detection System for In-Vehicle Networkcited by 42
  5. 2019CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Datacited by 29
  6. 2020A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Datasetcited by 19
  7. 2020CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Datacited by 9
  8. 2022CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Levelcited by 9
  9. 2021Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphscited by 8
  10. 2022Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoderscited by 7
  11. 2023X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Networkcited by 7
  12. 2021GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Buscited by 6
2 more
  1. 2020Graph-Based Intrusion Detection System for Controller Area Networkscited by 5
  2. 2019LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communicationscited by 3

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