An Intrusion Detection Algorithm and Its Hardware Acceleration for CAN in Vehicles

Xu He; Wu Di; Lu Jiwu; Li Renfa

doi:10.7544/issn1000-1239.202220035

Journal of Computer Research and Development > 2023 > 60(12): 2783-2796. > DOI: 10.7544/issn1000-1239.202220035

Xu He, Wu Di, Lu Jiwu, Li Renfa. An Intrusion Detection Algorithm and Its Hardware Acceleration for CAN in Vehicles[J]. Journal of Computer Research and Development, 2023, 60(12): 2783-2796. DOI: 10.7544/issn1000-1239.202220035

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An Intrusion Detection Algorithm and Its Hardware Acceleration for CAN in Vehicles

Xu He^1,,
Wu Di^{2, 3,},
Lu Jiwu^1, ,,
Li Renfa^{2, 3}

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
2.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082
3.
Key Laboratory for Embedded and Network Computing of Hunan Province (Hunan University), Changsha 410082

Funds: This work was supported by the National Natural Science Foundation of China (61932010， 61972145) and the National Natural Science Foundation of Hunan Province (2023JJ30146)

More Information

Author Bio:
Xu He: born in 1996. Master candidate. His main research interests include data anomaly detection and machine learning

Wu Di: born in 1982. PhD, professor. Member of IEEE. His main research interests include IoT, urban computing, and intelligent transportation

Lu Jiwu: born in 1977. PhD， professor. Member of IEEE. His main research interests include edge computing in IoT and power semiconductor devices

Li Renfa: born in 1957. PhD， professor. Senior member of IEEE. His main research interests include computer architecture, embedded computing， CPS, and IoT
Received Date: January 08, 2022
Revised Date: January 29, 2023
Available Online: September 19, 2023

Graphical Abstract

Abstract

Abstract

A controller area network (CAN) bus protocol is widely used in the vehicular system and is an efficient standard bus enabling communication between all electronic control units (ECUs). However, the CAN bus is easy to be attacked because of a lack of security defense features. We propose self-attention mechanism (SAM) enhanced grid long short-term memory (Grid LSTM) for vehicular intrusion detection, namely SALVID. The SAM can enhance the characteristics of CAN bus-oriented attack behavior, and the Grid LSTM can effectively extract the depth features of time series data. We generate five attack datasets by extracting benign CAN data from the actual car, including denial of service (DoS), fuzzy, spoofing, replay, and delete attacks. We compare the performance of various models with different model depths, and the results demonstrate that SALVID has the best performance in detecting the attacks on CAN bus. SALVID can identify attacks with small-batch features according to an overall detection accuracy of 98.98%, which is hard to be done in previous studies. We also design and implement SALVID based on field programmable gate array (FPGA) embedded platform and use parallel optimization and quantification to accelerate the model based on previous experiments. Even with a certain degree of quantification, SALVID still displays high detection accuracy of 98.81% and a latency of 1.88 ms. The investigation provides a new idea for designing high-performance and real-time vehicular intrusion detection systems.
- CAN,
- time-series data,
- intrusion detection,
- Grid LSTM,
- SAM,
- FPGA

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References (25)

References

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