Abstract
Vehicular Ad-hoc Network (VANET) is a heterogeneous network of resource-constrained nodes such as smart vehicles and Road Side Units (RSUs) communicating in a high mobility environment. Concerning the potentially malicious misbehaves in VANETs, real-time and robust intrusion detection methods are required. In this paper, we present a novel Machine Learning (ML) based intrusion detection methods to automatically detect intruders globally and locally in VANETs. Compared to previous Intrusion Detection methods, our method is more robust to the environmental changes that are typical in VANETs, especially when intruders overtake senior units like RSUs and Cluster Heads (CHs). The experimental results show that our approach can outperform previous work significantly when vulnerable RSUs exist.
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Acknowledgement
This work is supported by China NSFC 61836005 and 61672358; China NSFC 61728303 and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT1800417).
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Zeng, Y., Qiu, M., Ming, Z., Liu, M. (2018). Senior2Local: A Machine Learning Based Intrusion Detection Method for VANETs. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_41
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DOI: https://doi.org/10.1007/978-3-030-05755-8_41
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