EASI: Edge-Based Sender Identification on Resource-Constrained Platforms for Automotive Networks
Marcel Kneib, Oleg Schell, Christopher Huth
Abstract
In vehicles, internal Electronic Control Units (ECUs) are increasingly prone to adversarial exploitation over wireless connections due to ongoing digitalization. Controlling an ECU allows an adversary to send messages to the internal vehicle bus and thereby to control various vehicle functions. Access to the Controller Area Network (CAN), the most widely used bus technology, is especially severe as it controls brakes and steering. However, state of the art receivers are not able to identify the sender of a frame. Retrofitting frame authenticity, e.g. through Message Authentication Codes (MACs), is only possible to a limited extent due to reduced bandwidth, low payload and limited computational resources. To address this problem, observation in analog differences of the CAN signal was proposed to determine the actual sender. Some of the prior approaches exhibit good identification and detection rates, however require high sampling rates and a high computing effort. With EASI we significantly reduce the required resources and at the same time show increased identification rates of 99.98% by having no false positives in a prototype structure and two series production vehicles. In comparison to the most lightweight approach so far, we have reduced the memory footprint and the computational requirements by a factor of 168 and 142, respectively. In addition, we show the feasibility of EASI and thus demonstrate for the first time that voltage-based sender identification is realizable using comprehensive signal characteristics on resource-constrained platforms. Due to the lightweight design, we achieved a classification in under 100μs with a training time of 2.61 seconds. We also showed the ability to adapt the system to incremental signal changes during operation. Since cost effectiveness is of utmost importance in the automotive industry due to high production volumes, the achieved improvements are significant and necessary to realize sender identification.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers7
- CANNON: Reliable and Stealthy Remote Shutdown Attacks via Unaltered Automotive MicrocontrollersSekar Kulandaivel, Shalabh Jain, Jorge Guajardo, Vyas SekarS&P 2021 · 35 citations
- Exposing New Vulnerabilities of Error Handling Mechanism in CANKhaled Serag, Rohit Bhatia, Vireshwar Kumar, Z. Berkay Celik et al.USENIX Security 2021 · 30 citations
- "Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security ConferencesDaniel Olszewski, Allison Lu, Carson Stillman, Kevin Warren et al.CCS 2023 · 19 citations
- ZBCAN: A Zero-Byte CAN Defense SystemKhaled Serag, Rohit Bhatia, Akram Faqih, Muslum Ozgur Ozmen et al.USENIX Security 2023
- Evading Voltage-Based Intrusion Detection on Automotive CANRohit Bhatia, Vireshwar Kumar, Khaled Serag, Z. Berkay Celik et al.NDSS 2021
Builds on5
- Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression LearningMatthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu et al.S&P 2018 · 867 citations
- Fingerprinting Electronic Control Units for Vehicle Intrusion DetectionKyong-Tak Cho, Kang G. ShinUSENIX Security 2016 · 524 citations
- Error Handling of In-vehicle Networks Makes Them VulnerableKyong-Tak Cho, Kang G. ShinCCS 2016 · 238 citations
- Viden: Attacker Identification on In-Vehicle NetworksKyong-Tak Cho, Kang G. ShinCCS 2017 · 218 citations
- Scission: Signal Characteristic-Based Sender Identification and Intrusion Detection in Automotive NetworksMarcel Kneib, Christopher HuthCCS 2018 · 162 citations
Related papers
- EdgeTDC: On the Security of Time Difference of Arrival Measurements in CAN Bus SystemsMarc Roeschlin, Giovanni Camurati, Pascal Brunner, Mridula Singh et al.NDSS 2023
- CANvas: Fast and Inexpensive Automotive Network MappingSekar Kulandaivel, Tushar Goyal, Arnav Kumar Agrawal, Vyas SekarUSENIX Security 2019 · 49 citations
- RIDAS: Real-time identification of attack sources on controller area networksJiwoo Shin, Hyunghoon Kim, Seyoung Lee, Wonsuk Choi et al.USENIX Security 2023
- LibreCAN: Automated CAN Message TranslatorMert D. Pesé, Troy Stacer, C. Andrés Campos, Eric Newberry et al.CCS 2019 · 76 citations
- Vulnerability of Controller Area Network to Schedule-Based AttacksSena Hounsinou, Mark Stidd, Uchenna Ezeobi, Habeeb Olufowobi et al.RTSS 2021 · 16 citations
