车联网中基于神经网络的入侵检测方案

doi:10.3969/j.issn.1000-436x.2014.z2.032

Abstract

Abstract:

Vehicle networking intrusion detection solutions (IDS) can be used to confirm the authenticity of the events described in the notice of traffic incidents.The current Vehicle networking IDS frequently use detection scheme based on the consistency of redundant data,to reduce dependence on redundant data,an intrusion detection scheme based on neural network is presented.The program can be described as a lot of traffic event types ,and the integrated use of the back-propagation (BP) and support vector machine (SVM) two learning algorithms.The two algorithms respectively applicable to personal safety driving fast and efficient transportation system with high detection applications.Simulation results and performance analysis show that our scheme has a faster speed intrusion detection,and has a high detection rate and low false alarm rate.

Key words: vehicle networking, intrusion detection, neural network, BP, SVM

Yi-liang LIU,Ya-li SHI,Hao FENG,Liang-min WANG. Intrusion detection scheme based on neural network in vehicle network[J]. Journal on Communications, 2014, 35(Z2): 233-239.

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References 12

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机制	方案	实时性	已知入侵检测	未知异常检测	单跳	多跳
一致性	文献[5]	—	已知入侵检测	—	单跳	—
	文献[6，7，8]	实时性	已知入侵检测	未知异常检测	—	多跳
信任	文献[9]	实时性	已知入侵检测	—	单跳	—
投票	文献[10]	实时性	—	—	单跳	—
统计	文献[11]	实时性	已知入侵检测	未知异常检测	单跳	—

学习算法		BP	SVM
	时间	19.63	1 125.4
训练	方差	0.118 3	0.113 7
	均方差	0.003 9	0.000 9
	时间	0.026	5.320
测试	方差	0.015 5	0.011 8
	均方差	0.005 6	0..001 7

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Intrusion detection scheme based on neural network in vehicle network

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