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

arXiv: https://arxiv.org/abs/1907.07377 · HTML: https://mlsanity.com/paper/1907.07377/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 30 of 42)

- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks](https://mlsanity.com/paper/2107.05172/) (2021)
- [CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level](https://mlsanity.com/paper/2205.01306/) (2022)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus](https://mlsanity.com/paper/2108.10908/) (2021)
- [LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles](https://mlsanity.com/paper/2208.03399/) (2022)
- [Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey](https://mlsanity.com/paper/2404.16356/) (2024)
- [Roadmap for Cybersecurity in Autonomous Vehicles](https://mlsanity.com/paper/2201.10349/) (2022)
- [Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs](https://mlsanity.com/paper/2401.10724/) (2024)
- [SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks](https://mlsanity.com/paper/2108.08262/) (2021)
- [AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks](https://mlsanity.com/paper/2407.03506/) (2024)
- [STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles](https://mlsanity.com/paper/2204.10990/) (2022)
- [Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System](https://mlsanity.com/paper/2207.10814/) (2022)
- [SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification](https://mlsanity.com/paper/2311.12074/) (2023)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data](https://mlsanity.com/paper/2209.02052/) (2022)
- [A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs](https://mlsanity.com/paper/2401.10689/) (2024)
- [Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN](https://mlsanity.com/paper/2401.11030/) (2024)
- [SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic](https://mlsanity.com/paper/2402.14862/) (2024)
- [Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries](https://mlsanity.com/paper/2504.01905/) (2025)
- [Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity](https://mlsanity.com/paper/2110.14078/) (2021)
- [Anomaly Detection in Intra-Vehicle Networks](https://mlsanity.com/paper/2205.03537/) (2022)
- [Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems](https://mlsanity.com/paper/2207.04843/) (2022)
- [STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning](https://mlsanity.com/paper/2311.07056/) (2023)
- [LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection](https://mlsanity.com/paper/2311.08000/) (2023)
- [ADVENT: Attack/Anomaly Detection in VANETs](https://mlsanity.com/paper/2401.08564/) (2024)
- [Deep Learning-based Embedded Intrusion Detection System for Automotive CAN](https://mlsanity.com/paper/2401.10674/) (2024)

## 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.