Specification-based Intrusion Detection Method for Greedy Perimeter Stateless Routing

doi:10.3969/j.issn.1000-3428.2017.04.023

Abstract

Abstract: Aimming at the problem that Greedy Perimeter Stateless Routing(GPSR) protocol is widely applied in Mobile Ad Hoc Network(MANET) and there is no proper intrusion detection technology for it,based on the analysis of intrusion detection technology for MANET,this paper proposes an intrusion detection method for GPSR according to the characteristic of GPSR protocol and typical attacks it faces.Five detection constraints are provided,according to which it constructs the basic framework and running process of the intrusion detection algorithm.Experimental results show that the proposed intrusion detection method has high detection rate and low false alarm rate when being used to detect typical attacks.

Key words: Mobile Ad hoc Network(MANET), Greedy Perimeter Stateless Routing(GPSR) protocol, intrusion detection, detection rule, typical attack

摘要： 贪婪边界无状态路由(GPSR)协议在移动自组织网络中的应用较多,而相应的入侵检测技术则不够完善。为此,在分析移动自组织网络入侵检测技术的基础上,根据GPSR协议的特点和面临的典型攻击,提出一种GPSR入侵检测方法,制定5条检测规则,并根据规则设计入侵检测算法的基本框架和运行流程。仿真结果表明,该方法对典型攻击具有较高的检测率和较低的误报率。

关键词: 移动自组织网络, 贪婪边界无状态路由协议, 入侵检测, 检测规则, 典型攻击

CLC Number:

TP309.2

XU Fu. Specification-based Intrusion Detection Method for Greedy Perimeter Stateless Routing[J]. Computer Engineering.

徐甫. 基于规范的贪婪边界无状态路由入侵检测方法[J]. 计算机工程.

/ Recommend / Download Citations

URL:

https://www.ecice06.com/EN/Y2017/V43/I4/133

References

参考文献［1］易平,蒋嶷川,张世永,等.移动Ad Hoc网络安全综述［J］.电子学报,2005,33(5):893-899. ［2］钱钊.基于位置信息的移动自组织网络路由算法研究［D］.哈尔滨:哈尔滨工业大学,2013. ［3］Karp B,Kung H T.GPSR:Greedy Perimeter Stateless Routing for Wireless Networks［C］//Proceedings of the 6th International Conference on Mobile Computing and Networking.Boston,USA:ACM Press,2000:243-254. ［4］Zhang Yongguang,Lee W.Intrusion Detection in Wireless Ad-hoc Networks［C］//Proceedings of the 6th International Conference on Mobile Computing and Networking.Boston,USA:ACM Press,2000:275-283. ［5］Zhang Yongguang,Lee W.Intrusion Detection Techniques for Mobile Wireless Networks［J］.Mobile Networks and Applications,2003,9(5):545-556. ［6］Kachirski O,Guha R.Intrusion Detection Using Mobile Agents in Wireless Ad Hoc Networks［C］//Proceedings of IEEE Workshop on Knowledge Media Networking.Kyoto,Japan:IEEE Computer Society,2002:153-158. ［7］姚越鹏,钟求喜.基于代理的分级MANET入侵检测系统［J］.计算机工程,2009,35(3):192-194. ［8］Huang Yi-an,Lee W.A Cooperative Intrusion Detection System for Ad Hoc Networks［C］//Proceedings of ACM Workshop on Security of Ad Hoc and Sensor Networks.New York,USA:ACM Press,2003:135-147. ［9］Cretu G F,Parekh J,Wang Ke,et al.Intrusion and Anomaly Detection Model Exchange for Mobile Ad-Hoc Networks［C］//Proceedings of IEEE Consumer Com-munication and Networking Conference.Las Vegas,USA:IEEE Communications Society,2006:635-639. ［10］Jabbehdari S,Talari S H,Modiri N.A Neural Network Scheme for Anomaly Based Intrusion Detection Systems in Mobile Ad Hoc Networks［J］.Journal of Computing,2012,4(2):61-66. ［11］Vgina G,Gawalani S,Srinivasan K,et al.An Intrusion Detection Tool for AODV Based Ad Hoc Wireless Networks［C］//Proceedings of IEEE Annual Computer Security Application Conference.Tucson,USA:IEEE Computer Society,2004:16-27. ［12］Subhadrabandhu D,Sarkar S,Anjum F.A Framework for Misuse Detection in Ad Hoc Networks［J］.IEEE Journal on Selected Areas in Communications,2006,24(2):274-289. ［13］Nadeem A,Michael P H.A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks［J］.IEEE Communications Surveys & Tutorials,2013,15(4):2027-2045. ［14］Tseng C Y,Balasubramanyam P,Ko C,et al.A Specification-based Intrusion Detection System for AODV［C］//Proceedings of ACM Workshop on Security of Ad Hoc and Sensor Networks.Fairfax,USA:ACM Press,2003:125-134. ［15］Hassan H M,Mahmoud,El-Kassas S.Securing the AODV Protocol Using Specification-based Intrusion Detection［C］//Proceedings of Q2SWinet’06.Torremolinos,Spain:ACM Press,2006:33-36. ［16］Lin Hsiao-ching,Sun Ming-kung,Huang Hanwei,et al.A Specification-based Intrusion Detection Model for Wireless Ad Hoc Networks［C］//Proceedings of the 3rd International Conference on Innovations in Bio-Inspired Computing and Applications.Washington D.C.,USA:IEEE Press,2012:252-257. ［17］易平,柳宁,吴越.基于时间自动机的Ad hoc网络入侵检测［J］.电子与信息学报,2009,31(19):2310-2315. ［18］易平.移动Ad Hoc网络入侵检测与主动响应机制研究［D］.上海:复旦大学,2005. ［19］王芳,易平,吴越,等.基于规范的移动Ad Hoc网络分布式入侵检测［J］.计算机科学,2010,37(10):118-122. ［20］Orset J M,Alcalde B,Cavalli A.An EFSM-based Intrusion Detection System for Ad Hoc Networks［C］//Proceedings of the 3rd International Symposium on Automated Technology for Verification and Analysis.Berlin,Germany:Springer,2005:399-412. ［21］Tseng C H,Song T,Balasubramanyam P,et al.A Specification-based Intrusion Detection Model for OLSR［C］//Proceedings of the 8th International Symposium on Recent Advances in Intrusion Detection.Berlin,Germany:Springer,2005:330-350. 编辑顾逸斐

[1]	YU Changhong, XU Konghao, ZHANG Ze, GAO Ming. Improving Network Intrusion Detection Methods in Isolated Forests Based on Split Points [J]. Computer Engineering, 2024, 50(6): 148-156.
[2]	ZHANG Huiyan, LIANG Yong, LAN Jinghong, ZHAO Qiang. Intrusion Detection Method Based on Memory Module and Filtered Generative Adversarial Network [J]. Computer Engineering, 2024, 50(6): 197-207.
[3]	Hong CHEN, Hanwen WANG, Haibo JIN. Intrusion Detection Model Combining Improved Self-Encoder and Residual Network [J]. Computer Engineering, 2024, 50(2): 188-195.
[4]	SHEN Xueli, LIU Shifeng. Intrusion Detection Model Based on Graph Edge Feature Attention [J]. Computer Engineering, 2024, 50(11): 236-245.
[5]	CHEN Zhonglei, YI Peng, CHEN Xiang, HU Tao. Real-time Anomaly Detection Framework via System Calls Based on Integrated Learning [J]. Computer Engineering, 2023, 49(6): 162-169,179.
[6]	SHI Lei, ZHANG Jitao, GAO Yufei, WEI Lin, TAO Yongcai. Intrusion Detection of Network Traffic Based on Transformer and BiLSTM [J]. Computer Engineering, 2023, 49(3): 29-36,57.
[7]	LIU Qiang, ZHANG Ying, ZHOU Weixiang, JIANG Xiantao, ZHOU Weina, ZHOU Mouguo. Adaptive Class Incremental Learning-Based IoT Intrusion Detection System [J]. Computer Engineering, 2023, 49(2): 169-174.
[8]	Tianyu CHEN, Chengqian CHU, Siyuan WAN, Yongjing WAN, Jing SUN. Video Intrusion Detection Algorithm Based on Conditional Lightweight Neural Network [J]. Computer Engineering, 2023, 49(12): 152-160.
[9]	LIU Jinshuo, ZHAN Daiyi, DENG Juan, WANG Lina. Network Intrusion Detection Based on Deep Neural Network and Federated Learning [J]. Computer Engineering, 2023, 49(1): 15-21,30.
[10]	SUN Yangwei, QI Yong. Intrusion Detection Method for In-Vehicle CAN Based on Cluster Mixed Sampling and PSO-Stacking [J]. Computer Engineering, 2023, 49(1): 138-145.
[11]	DONG Weiyu, LI Haitao, WANG Ruimin, REN Huajuan, SUN Xuekai. Network Traffic Anomaly Detection Model Based on Stacked Convolutional Attention [J]. Computer Engineering, 2022, 48(9): 12-19.
[12]	JIN Haibo, ZHAO Xinyue. High-Reliability Intrusion Detection Algorithm Under Conformal Prediction Framework [J]. Computer Engineering, 2022, 48(7): 130-140.
[13]	SHENG Long, YUAN Lina, WU Nannan, JI Shaopei. Network Anomaly Detection Model Based on GSA and DE Optimizing Hybrid Kernel ELM [J]. Computer Engineering, 2022, 48(6): 146-153.
[14]	LI Jinguo, JIAO Xubin. Research on Intrusion Detection Model in Fog Computing Environment [J]. Computer Engineering, 2022, 48(5): 43-52.
[15]	REN Zhi, ZHOU Zhou, WU Benyuan, CHEN Jialin. Low-Cost Topology Maintenance Algorithm for Optimized Link State Routing Protocol [J]. Computer Engineering, 2021, 47(9): 120-127,135.

Please choose a citation manager

Content to export