车联网中基于神经网络的入侵检测方案

刘怡良; 石亚丽; 冯蒿; 王良民

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

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车联网中基于神经网络的入侵检测方案

学术通信 | 更新时间：2024-06-05

- 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
- 通信学报 2014年35卷第Z2期页码：233-239
- 作者机构：
  
  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
- 作者简介：
  
  [ "刘怡良（1990-），男，江苏徐州人，江苏大学硕士生，主要研究方向为车联网、网络安全。" ]
  [ "石亚丽（1992-），女，安徽芜湖人，江苏大学硕士生，主要研究方向为车联网安全。" ]
  [ "冯蒿（1979-），男，河南新密人，国网三门峡供电公司工程师，主要研究方向为电力系统自动化、继电保护。" ]
  [ "王良民（1977-），男，安徽潜山人，江苏大学教授、博士生导师，主要研究方向为物联网信息处理技术、物联网安全协议、车联网安全结构。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61272074);江苏省自然科学基金资助项目(BK2011464);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(GY2013030)
- DOI：10.3969/j.issn.1000-436x.2014.z2.032
  中图分类号： TP393
- 网络出版日期：2014-11，
  
  纸质出版日期：2014-11-30
- 稿件说明：
移动端阅览
刘怡良, 石亚丽, 冯蒿, 等. 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, et al. Intrusion detection scheme based on neural network in vehicle network[J]. Journal on communications, 2014, 35(Z2): 233-239.
刘怡良, 石亚丽, 冯蒿, 等. 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. DOI： 10.3969/j.issn.1000-436x.2014.z2.032.

Yi-liang LIU, Ya-li SHI, Hao FENG, et al. Intrusion detection scheme based on neural network in vehicle network[J]. Journal on communications, 2014, 35(Z2): 233-239. DOI： 10.3969/j.issn.1000-436x.2014.z2.032.

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。

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.

关键词

Keywords

references

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RAYA M , PAPADIMITRATOS P , AAD I , et al . Eviction of misbehaving and faulty nodes in vehicular networks [J ] . IEEE Journal on Selected Areas in Communications , 2007 , 25 ( 8 ): 1557 - 1568 .

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LI X J , WANG L M . A rapid certification protocol from bilinear pairing for vehicular ad hoc networks [A ] . Trust,Security and Privacy in Computing and Communcations [C ] . 2012 . 890 - 895 .

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