基于朋友机制的移动ad hoc网络路由入侵检测模型研究

doi:10.11959/j.issn.1000-436x.2015301

Abstract

Abstract:

The proposed model mainly focused on how to effectively detect routing invasions from mobile ad hoc network as well as how to accurately respond to the malicious nodes,providing a trusted routing environment.A lightweight intrusion detection model was proposed to based on the friends mechanism,taking black hole attack for example,the OPNET software gives the simulation test,and results show that the schemes can effectively detect attacks,it is validated and compared with other conventional models.

Key words: mobile ad hoc network, routing security, intrusion detection, friends mechanisms

Yang XIAO,Lei BAI,Xian WANG. Friends mechanism-based routing intrusion detection model for mobile ad hoc network[J]. Journal on Communications, 2015, 36(Z1): 203-214.

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References 20

[1]	YANG C T , et al. A specification-based intrusion detection system for AODV[A]. Proc of the ACM Workshop on security of Ad Hoc and SensorNetworks[C]. 2003. 125-134.
[2]	HUANG Y , FAN W , LEE W , et al. Cross-featurea nalysisf or detecting ad-hoc routing anomalies[A]. Proceedings of the 23rd Intemational Conference On Distributed Computing Systems[C]. 2003. 478-487.
[3]	SRINIVASARAO G , KUMAR S S . An Efficient Intrusion Detection System in Mobile Ad hoc Networks[A]. Lecture Notes in Electrical Engineering 151[C]. 2013.
[4]	KULKARNI P . Design of hierarchical intrusion detection unit for ad hoc networks based on bayesian networks[M]. Dissertations ＆ These grad works, 2008.
[5]	PERRIG A , ZAPATA , JOHNSON D B . Ariadne:a secure on-demand routing protocol for ad hoc networks[A]. Mobile Computing and Networking ACM[C]. 2002. 12-23.
[6]	JOHN C E . Fast training of support vector machines using sequential minimal optimization[M]. Advances in Kernel Methods Support Vector Learning,Cambridge,MA,M/T Press, 1999: 185-208.
[7]	KEERTHI S S , SHEVADE S K , BHATTACHARYYA C , et al. Improvements to Platt’s SM4 algorithm for SVM classifier design[D]. Dept.of O Mechanical and Production Engineering,National University of Singapore, 1999.
[8]	JOACHIMS T . Making Large-Scale SVM Learning Practical[R]. LS8Report,University of Dortmund, 1998.
[9]	CHANG C C , LIN C J . LIBSVM:a library for support vector machines[EB/OL]. , 2001.
[10]	MILGRAM S . The small world problem[J]. Psychology Today, 1967,(5): 60-67.
[11]	HELMY A . Small worlds in wireless networks[J]. IEEE Communications Letters, 2003,7(10): 490-492.
[12]	CAPKUN S , HUBAUX J P , BUTTYAN L . Mobility helps security in ad hoc networks[A]. Proc of MobiHoc’03,Annapolis[C]. 2003. 45-56.
[13]	PEI W D , CHEN Z Q , YUAN Z Z . Friends-help mechanism for generating a class of scale-free networks[J]. Journal of Jilin University (Information Science Edition) 2007,25(4): 371-378.
[14]	MA C E SHI H S . Security model and mathematical analysis of mobile ad hoc network[J]. Chinese Journal of Sensors and Actuators, 2006,19(4): 1305-1309.
[15]	RAGHAVAN B , SNOEREN A C . Priority forwarding in ad hoc networks with self-interested parties[A]. Workshop on Economics of Peer-to-Peer Systems[C]. Berkeley,USA,May, 2003.
[16]	MIRANDA H , RODRIGUES L . Friends and foes:preventing selfishness in open mobile ad hoc networks[A]. Proc of the First International Workshop on Mobile Distributed Computing (MDC’03)[C]. USA, 2003.
[17]	VAPNIK V N . The nature of statistical learning theory[A]. Series:Information Science and Statistics[C]. 1996.
[18]	RAZAK S A , FURNELL S , CLARKE N , et al. A two-tier intrusion detection system for mobile ad hoc networks–a friend approach[A]. Intelligence and Security Informatics[C]. Springer Berlin Heidelberg, 2006. 590-595.
[19]	DENG H M , ZENG Q A , AGRAWAL D P . SVM-based intrusion detection system for wireless ad hoc networks[A]. Vehicular Technology Conference[C]. 2003.
[20]	WANG X ; WONG J S , STANLEY F , BASU S . Cross-layer based anomaly detection in wireless mesh networks[A]. Ninth Annual International Symposium on Applications and the Internet[C]. 2009. 20-24.

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节点	初始信任节点
A	B,C
B	C,D,E
C	A,D,B
D	C,B
E	A,C

误用检测引擎MDE	异常检测引擎ADE	MDE^ ADE	状态
1	0	1	Intruder
0	1	1	Intruder
1	1	1	Intruder
0	0	0	Friend

节点	信任等级
A	2/5
B	3/5
C	4/5
D	2/5
E	1/5

输入特征参数个数	数据样本	核函数	相关参数（C，γ）	CPU运行时间/s	错误分类	支持向量
3	3 568	Linear	DEFAULT	182.17	89	271
3	3 568	Linear	0.5,0.5	37.60	1 236	16
3	3 568	Linear	1.0,0.5	2.86	2 306	14
3	3 568	Linear	1.0,1.0	13.15	2 306	14
3	3 568	Linear	2.0,1.0	2.93	2 306	12
3	3 568	Radial	DEFAULT	2.29	54	935
3	3 568	Radial	0.5,0.5	1.79	51	810
3	3 568	Radial	1.0,0.5	2.14	37	796
3	3 568	Radial	1.0,1.0	3.38	39	923
3	3 568	Radial	2.0,1.0	2.75	36	902
3	3 568	Sigmoid	DEFAULT	1.48	1 235	2 396
3	3 568	Sigmoid	0.5,0.5	1.59	1 235	2 396
3	3 568	Sigmoid	1.0,0.5	1.33	1 235	2 396
3	3 568	Sigmoid	1.0,1.0	1.36	1 235	2 396
3	3 568	Sigmoid	2.0,1.0	1.48	1 235	2 396

输入特征参数个数	测试数据	核函数	正确个数	错误个数	IDS准确性/%	Precision/ Recall
3	3 568	Linear	3 479	89	97.50	99.78%/96.25%
3	3 568	Linear	2 332	1 236	65.36	65.05%/99.91%
3	3 568	Linear	1 262	2 306	35.37	50%/0.09%
3	3 568	Linear	1 262	2 306	35.37	50%/0.09%
3	3 568	Linear	1 262	2 306	35.37	50%/0.09%
3	3 568	Radial	3 514	54	98.48	98.63%/98.97%
3	3 568	Radial	3 517	51	98.57	98.84%/98.92%
3	3 568	Radial	3 531	37	98.96	99.39%/98.92%
3	3 568	Radial	3 529	39	98.90	99.35%/98.92%
3	3 568	Radial	3 532	36	98.99	99.74%/99.0%
3	3 568	Sigmoid	2 333	1 235	65.39	65.05%/100%
3	3 568	Sigmoid	2 333	1 235	65.39	65.05%/100%
3	3 568	Sigmoid	2 333	1 235	65.39	65.05%/100%
3	3 568	Sigmoid	2 333	1 235	65.39	65.05%/100%
3	3 568	Sigmoid	2 333	1 235	65.39	65.05%/100%

Friends mechanism-based routing intrusion detection model for mobile ad hoc network

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References 20

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序号	相关模型	检测率	误报率
1	1-SVMDM	85.58%	20.85%
2	2- SVMDM	96.95%	4.86%
3	SVM模型	98.20%	0.30%
4	本文提出的模型	99.00%	0.26%

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