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

Abstract

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.

arXiv comment9 pages, 11 figures, Accepted for PST 2018 : 16th International Conference on Privacy, Security and Trust, some numbers in table 2 has fixed after doing additional experiment

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• Benchmarking Frameworks and Comparative Studies of Controller Area Network (CAN) Intrusion Detection Systems: A Review
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• A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles
• Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack
• Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
• Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluation
• Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs
• Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity
• Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
• CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation
• LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
• STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
• Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphs
• Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection