Detecting Vehicle Anomaly in the Edge via Sensor Consistency and Frequency Characteristic
Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, the authors propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Guo, Fei
- Wang, Zichang
- Du, Suguo
- Li, Huaxin
- Zhu, Haojin
- Pei, Qingqi
- Cao, Zhenfu
- Zhao, Jianhong
- Publication Date: 2019-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 5618-5628
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 68
- Issue Number: 6
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Computer architecture; Computer security; Correlation analysis; Edge detection; Frequency domain analysis; Sensors; Time domain analysis
- Uncontrolled Terms: Anomaly detection
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01710865
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 16 2019 4:32PM