mlsanityData to 7 Oct 2026

GIDS: GAN based Intrusion Detection System for In-Vehicle Network

arXiv 1907.07377 ↗First posted 17 Jul 2019cs.CRcited by 42

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Abstract (arXiv)

A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 9; 2023: 3; 2024: 15; 2025: 6; 2026: 2.

42 papers.

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  1. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  2. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  3. 2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
  4. 2022A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehiclescited by 11
  5. 2021Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networkscited by 10
  6. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  7. 2022CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Levelcited by 8
  8. 2021GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Buscited by 6
  9. 2022LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehiclescited by 6
  10. 2024Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Surveycited by 6
  11. 2022Roadmap for Cybersecurity in Autonomous Vehiclescited by 5
  12. 2024Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAscited by 5
30 more
  1. 2021SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networkscited by 3
  2. 2024AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networkscited by 3
  3. 2022STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehiclescited by 2
  4. 2022Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection Systemcited by 2
  5. 2023SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classificationcited by 2
  6. 2022RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN datacited by 1
  7. 2024A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAscited by 1
  8. 2024Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CANcited by 1
  9. 2024SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Trafficcited by 1
  10. 2024A Survey of Anomaly Detection in In-Vehicle Networkscited by 1
  11. 2025Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Librariescited by 1
  12. 2021Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecuritycited by 0
  13. 2022Anomaly Detection in Intra-Vehicle Networkscited by 0
  14. 2022Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systemscited by 0
  15. 2023STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learningcited by 0
  16. 2023LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detectioncited by 0
  17. 2024ADVENT: Attack/Anomaly Detection in VANETscited by 0
  18. 2024Deep Learning-based Embedded Intrusion Detection System for Automotive CANcited by 0
  19. 2024A Lightweight FPGA-based IDS-ECU Architecture for Automotive CANcited by 0
  20. 2024MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Networkcited by 0
  21. 2024CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulationcited by 0
  22. 2024The Roles of Generative Artificial Intelligence in Internet of Electric Vehiclescited by 0
  23. 2024MDHP-Net: Detecting an Emerging Time-exciting Threat in IVNcited by 0
  24. 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
  25. 2025A Scalable Hierarchical Intrusion Detection System for Internet of Vehiclescited by 0
  26. 2025Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulationcited by 0
  27. 2025Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detectioncited by 0
  28. 2025FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detectioncited by 0
  29. 2026DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehiclescited by 0
  30. 2026MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mambacited by 0