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

通信学报 ›› 2014, Vol. 35 ›› Issue (Z2): 233-239.doi: 10.3969/j.issn.1000-436x.2014.z2.032

车联网中基于神经网络的入侵检测方案

刘怡良¹,石亚丽¹,冯蒿²,王良民¹

¹ 江苏大学计算机科学与通信工程学院，江苏镇江 212013
² 国网三门峡供电公司，河南三门峡 472000

出版日期:2014-11-25 发布日期:2017-06-19
基金资助:
国家自然科学基金资助项目;江苏省自然科学基金资助项目;江苏省青蓝工程优秀中青年学术带头人;镇江市工业支撑基金资助项目

Intrusion detection scheme based on neural network in vehicle network

Yi-liang LIU¹,Ya-li SHI¹,Hao FENG²,Liang-min WANG¹

¹ School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China
² State Grid Power Company Sanmenxia,Sanmenxia 472000,China

Online:2014-11-25 Published:2017-06-19
Supported by:
The National Natural Science Foundation of China;The Natural Science Foundation of Jiangsu Province;Blue Project of Jiangsu Province Outstanding Young Academic Leaders;Zhenjiang City Industrial Support Project

摘要/Abstract

摘要：

车联网的入侵检测（IDS）可用于确认交通事件通知中描述的事件的真实性。当前车联网IDS多采用基于冗余数据的一致性检测方案，为降低IDS对冗余数据的依赖性，提出了一个基于神经网络的入侵检测方案。该方案可描述大量交通事件类型，并综合使用了反向传播（BP）和支持向量机（SVM）2种学习算法。这2种算法分别适用于个人安全驾驶速度快与高效交通系统检测率高的应用。仿真实验和性能分析表明，本方案具有较快的入侵检测速度,且具有较高的检测率和较低的虚警率。

关键词: 车联网, 入侵检测, 神经网络, BP, SVM

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

刘怡良,石亚丽,冯蒿,王良民. 车联网中基于神经网络的入侵检测方案[J]. 通信学报, 2014, 35(Z2): 233-239.

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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参考文献 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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车联网中基于神经网络的入侵检测方案

Intrusion detection scheme based on neural network in vehicle network

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