Intrusion detection scheme based on neural network in vehicle network
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 da...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.032/ |
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| _version_ | 1841539683543154688 |
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| author | Yi-liang LIU Ya-li SHI Hao FENG Liang-min WANG |
| author_facet | Yi-liang LIU Ya-li SHI Hao FENG Liang-min WANG |
| author_sort | Yi-liang LIU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bf82a08e264b48faae98241ff9eb3e6a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-bf82a08e264b48faae98241ff9eb3e6a2025-01-14T06:45:18ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-11-013523323959689495Intrusion detection scheme based on neural network in vehicle networkYi-liang LIUYa-li SHIHao FENGLiang-min WANGVehicle 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.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.032/vehicle networkingintrusion detectionneural networkBPSVM |
| spellingShingle | Yi-liang LIU Ya-li SHI Hao FENG Liang-min WANG Intrusion detection scheme based on neural network in vehicle network Tongxin xuebao vehicle networking intrusion detection neural network BP SVM |
| title | Intrusion detection scheme based on neural network in vehicle network |
| title_full | Intrusion detection scheme based on neural network in vehicle network |
| title_fullStr | Intrusion detection scheme based on neural network in vehicle network |
| title_full_unstemmed | Intrusion detection scheme based on neural network in vehicle network |
| title_short | Intrusion detection scheme based on neural network in vehicle network |
| title_sort | intrusion detection scheme based on neural network in vehicle network |
| topic | vehicle networking intrusion detection neural network BP SVM |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.032/ |
| work_keys_str_mv | AT yiliangliu intrusiondetectionschemebasedonneuralnetworkinvehiclenetwork AT yalishi intrusiondetectionschemebasedonneuralnetworkinvehiclenetwork AT haofeng intrusiondetectionschemebasedonneuralnetworkinvehiclenetwork AT liangminwang intrusiondetectionschemebasedonneuralnetworkinvehiclenetwork |