Title
Exploiting Temperature-Varied ECU Fingerprints for Source Identification in In-vehicle Network Intrusion Detection
Abstract
The in-vehicle controller area network(CAN) provides reliable communications among ECUs, whereas the lack of security design of CAN protocols makes it vulnerable to CAN targeting attacks. Unfortunately, existing CAN intrusion detection systems merely recognize fabricated CAN messages while only little work are devoted to intrusion source identification. Demonstrated by our experimental study, the state-of-the-art source ECU identification approaches, which are based on physical ECU fingerprints, will fail when ECU temperature varies. In this paper, we innovatively propose temperature-varied fingerprinting, called TVF, for CAN intrusion detection and source ECU identification. Inspired by the significant observation that the clock offset of a specific ECU, i.e., its fingerprint, varies with the environment temperature of the ECU, the concept of temperature-varied ECU fingerprints are proposed and exploited to improve source identification accuracy in real-world vehicle CAN intrusion cases. The proposed temperature-varied fingerprinting is implemented and extensive performance evaluation experiments are conducted in both CAN bus prototype and real vehicles. The experimental results demonstrate the efficacy of the proposed TVF.
Year
DOI
Venue
2019
10.1109/IPCCC47392.2019.8958766
2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)
Keywords
Field
DocType
In-vehicle network,controller area network,electronic control unit,source identification,intrusion detection
CAN bus,Intrusion,Clock offset,Computer science,Vehicle networks,Real-time computing,Fingerprint,Electronic control unit,Security design,Intrusion detection system
Conference
ISSN
ISBN
Citations 
1097-2641
978-1-7281-1026-4
0
PageRank 
References 
Authors
0.34
14
6
Name
Order
Citations
PageRank
Miaoqing Tian100.34
Ruobing Jiang212.72
Chaoqun Xing300.34
Haipeng Qu465.14
Qian Lu501.69
Xiaoyun Zhou600.34