Abstract
In recent years, a lot of vehicle attacks have been reported and demonstrated by researchers and whitehat hackers indicating vehicle cyber security as an important issue particularly for self-driving cars. The reason behind this extended attack vector is the multiple external interfaces of vehicles and minimal internal security protection. Hence, it is totally possible for adversaries to take full control of connected cars. In this paper, we propose an efficient Vehicular Intrusion Detection System (IDS), named as VIDS, which consists of a lightweight domain-based detection model for ECU devices and a comprehensive crossdomain-based detection model for a gateway or domain controller. The former makes use of specification periodic features of Controller Area Network (CAN) frames, while the latter exploits stream bit value features with deep learning techniques. With the use of real vehicular normal datasets and synthesized abnormal datasets for experimenting, the experimental results indicate that the proposed VIDS can achieve better detection rate over existing IDS systems. In addition, VIDS is compatible with vehicle internal CAN network.
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Notes
- 1.
IoVShield is multiple layers defense system for IoV, which consists of external network security, secure gateway, and internal network security.
- 2.
pandas is a software library written for Python programming language for data manipulation and analysis.
- 3.
sklearn is a free software library for machine learning implementations for Python programming language.
- 4.
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
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Acknowledgments
This work is supported by National Natural Science Funds of China (Grant No. 61402199, Grant No. U1636209) and Natural Science Funds of Guangdong (Grant No. 2015A030310017).
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Wei, Z., Yang, Y., Rehana, Y., Wu, Y., Weng, J., Deng, R.H. (2017). IoVShield: An Efficient Vehicular Intrusion Detection System for Self-driving (Short Paper). In: Liu, J., Samarati, P. (eds) Information Security Practice and Experience. ISPEC 2017. Lecture Notes in Computer Science(), vol 10701. Springer, Cham. https://doi.org/10.1007/978-3-319-72359-4_39
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DOI: https://doi.org/10.1007/978-3-319-72359-4_39
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