EASI: Edge-Based Sender Identification on Resource-Constrained Platforms for Automotive Networks
Marcel Kneib, Oleg Schell, Christopher Huth
摘要
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.
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引用它的顶会 Paper7
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- Evading Voltage-Based Intrusion Detection on Automotive CANRohit Bhatia, Vireshwar Kumar, Khaled Serag, Z. Berkay Celik 等NDSS 2021
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- Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression LearningMatthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu 等S&P 2018 · 被引用 867 次
- Fingerprinting Electronic Control Units for Vehicle Intrusion DetectionKyong-Tak Cho, Kang G. ShinUSENIX Security 2016 · 被引用 524 次
- Error Handling of In-vehicle Networks Makes Them VulnerableKyong-Tak Cho, Kang G. ShinCCS 2016 · 被引用 238 次
- Viden: Attacker Identification on In-Vehicle NetworksKyong-Tak Cho, Kang G. ShinCCS 2017 · 被引用 218 次
- Scission: Signal Characteristic-Based Sender Identification and Intrusion Detection in Automotive NetworksMarcel Kneib, Christopher HuthCCS 2018 · 被引用 162 次
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