Cyberattacks and Countermeasures For In-Vehicle Networks
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Abstract (arXiv)
As connectivity between and within vehicles increases, so does concern about safety and security. Various automotive serial protocols are used inside vehicles such as Controller Area Network (CAN), Local Interconnect Network (LIN) and FlexRay. CAN bus is the most used in-vehicle network protocol to support exchange of vehicle parameters between Electronic Control Units (ECUs). This protocol lacks security mechanisms by design and is therefore vulnerable to various attacks. Furthermore, connectivity of vehicles has made the CAN bus not only vulnerable from within the vehicle but also from outside. With the rise of connected cars, more entry points and interfaces have been introduced on board vehicles, thereby also leading to a wider potential attack surface. Existing security mechanisms focus on the use of encryption, authentication and vehicle Intrusion Detection Systems (IDS), which operate under various constrains such as low bandwidth, small frame size (e.g. in the CAN protocol), limited availability of computational resources and real-time sensitivity. We survey In-Vehicle Network (IVN) attacks which have been grouped under: direct interfaces-initiated attacks, telematics and infotainment-initiated attacks, and sensor-initiated attacks. We survey and classify current cryptographic and IDS approaches and compare these approaches based on criteria such as real time constrains, types of hardware used, changes in CAN bus behaviour, types of attack mitigation and software/ hardware used to validate these approaches. We conclude with potential mitigation strategies and research challenges for the future.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 1; 2026: 2.
8 papers.
- 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasurescited by 14
- 2022STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehiclescited by 2
- 2024A Survey of Anomaly Detection in In-Vehicle Networkscited by 1
- 2023LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detectioncited by 0
- 2024MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Networkcited by 0
- 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
- 2026DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehiclescited by 0
- 2026CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluationcited by 0
- 2026CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation29 Jun 2026
- 2026DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles22 Apr 2026
- 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network15 May 2025
- 2024A Survey of Anomaly Detection in In-Vehicle Networks11 Sep 2024
- 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures23 Apr 2024
- 2024MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network7 Mar 2024
- 2023LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection14 Nov 2023
- 2022STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles23 Apr 2022