This technical paper titled “SAID: State-aware Defense Against Injection Attacks on In-vehicle Network” was presented by researchers at the Hong Kong Polytechnic University, Zhejiang University, and Texas A&M University at the USENIX Security Symposium in Boston in August 2022.
Abstract:
“Modern vehicles are equipped with many ECUs (Electronic Control Unit) that are connected to the IVN (In-Vehicle Network) for controlling the vehicles. Meanwhile, various interfaces of vehicles, such as OBD-II port, T-Box, sensors, and telematics, implement the interaction between the IVN and external environment. Although rich value-added functionalities can be provided through these interfaces, such as diagnostics and OTA (Over The Air) updates, the adversary may also inject malicious data into IVN, thus causing severe safety issues. Even worse, existing defense approaches mainly focus on detecting the injection attacks launched from IVN, such as malicious/compromised ECUs, by analyzing CAN frames, and cannot defend against the higher layer MIAs (Message Injection Attacks) that can cause abnormal vehicle dynamics. In this paper, we propose a new state-aware abnormal message injection attack defense approach, named SAID. It detects the abnormal data to be injected into IVN by considering the data semantics and the vehicle dynamics and prevents the MIAs launched from devices connected to the vehicles, such as the compromised diagnostic tools and T-boxes. We develop a prototype of SAID for defending against MIAs and evaluate it using both real road data and simulation data. The experimental results show that SAID can defend against more than 99% of the network and service layer attack traffic and all state layer MIAs, effectively enforcing the safety of vehicles.”
Find the technical paper here (prepublication).
Authors:
Lei Xue, The Hong Kong Polytechnic University Shenzhen Research Institute; Yangyang Liu, Tianqi Li, Kaifa Zhao, Jianfeng Li, Le Yu, and Xiapu Luo, The Hong Kong Polytechnic University; Yajin Zhou, Zhejiang University; Guofei Gu, Texas A&M University.
