Intrusion Detection Method for MANET Based on Graph Theory
ZHANG Bingtao①② WANG Xiaopeng① WANG Lücheng① ZHANG Zhonglin① LI Yanlin③ LIU Hu①
①(School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070 China) ②(School of Information Science and Engineering, Lanzhou University, Lanzhou 730000 China) ③(Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000 China)
Abstract：Mobile Ad hoc NETwork (MANET) is vulnerable to various security threats, and intrusion detection is an effective guarantee for its safe operation. However, existing methods mainly focus on feature selection and feature weighting, and ignore the potential association among features. To solve this problem, an intrusion detection method for MANET based on graph theory is proposed. First of all, nine features are selected as nodes based on the analysis of typical attack behavior, and the edges among nodes are determined according to Euclidean distance so as to build the structure diagram. Secondly, the scale attributes of neighborhood nodes and the degree of closeness attributes among nodes are considered to explore (i.e. feature) the correlation among nodes, then the statistical properties degree distribution and clustering coefficient of graph theory are used to realize the above two attributes. Finally, contrasting experimental results show that compared with the traditional methods, the average detection rate and false detection rate of new method are improved by 10.15% and reduced by 1.8% respectively.
张冰涛,王小鹏,王履程,张忠林,李延林,刘虎. 基于图论的MANET入侵检测方法[J]. 电子与信息学报, 2018, 40(6): 1446-1452.
ZHANG Bingtao, WANG Xiaopeng, WANG Lücheng, ZHANG Zhonglin, LI Yanlin, LIU Hu. Intrusion Detection Method for MANET Based on Graph Theory. JEIT, 2018, 40(6): 1446-1452.
FENG Tao, GUO Xian, MA Jianfeng, et al. Provably secure approach for multiple node-disjoint paths source routing protocol[J]. Journal of Software, 2010, 21(7): 1717-1731. doi: 10.3724/SP.J.1001.2010.03576.
[2]
VADIVEL R and BHASKARAN V M. Adaptive reliable and congestion control routing protocol for MANET[J]. Wireless Networks, 2016, 23(3): 819-829. doi: 10.1007/s11276-015- 1137-3.
[3]
SINGAL G, LAXMI V, GAUR M S, et al. Multi-constraints link stable multicast routing protocol in MANETs[J]. Ad Hoc Networks, 2017, 63: 115-128. doi: 10.1016/j.adhoc.2017.05. 007.
[4]
INDIRANI G and SELVAKUMAR K. A swarm-based efficient distributed intrusion detection system for mobile Ad hoc networks (MANET)[J]. International Journal of Parallel, Emergent and Distributed Systems, 2014, 29(1): 90-103. doi: 10.1080/17445760. 2013.773001.
[5]
SINDHU S S S, GEETHA S, and KANNAN A. Decision tree based light weight intrusion detection using a wrapper approach[J]. Expert Systems with Applications, 2012, 39(1): 129-141. doi: 10.1016/j.eswa.2011.06.013.
[6]
FIDALCASTRO A and BABURAJ E. Sequential pattern mining for intrusion detection system with feature selection for MANETS[J]. Asian Journal of Research in Social Sciences and Humanities, 2017, 7(2): 428-442. doi: 10.5958/2249-7315. 2017.00100.9.
LI Hongcheng, WU Xiaoping, and YAN Bo. Research on distributed genetic k-means for anomaly detection in MANET[J]. Journal on Communications, 2015, 36(11): 167-173. doi: 10.11959/j.issn.1000-436x.2015269.
[8]
CHUNG Y Y and WAHID N. A hybrid network intrusion detection system using simplified swarm optimization (SSO)[J]. Applied Soft Computing, 2012, 12(9): 3014-3022. doi: 10.1016/j.asoc.2012.04.020.
[9]
LI Xiaojin, HU Xintao, JIN Changfeng, et al. A comparative study of theoretical graph models for characterizing structural networks of human brain[J]. International Journal of Biomedical Imaging, 2013, 13(1): 27-35. doi: 10.1155/2013/201735.
[10]
ZHU Guohun, LI Yan, and WEN P P. Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal[J]. IEEE Journal of Biomedical & Health Informatics, 2014, 18(6): 1813-1821. doi: 10.1109/JBHI.2014.2303991.
[11]
ZHANG Xiaowei, HU Bin, MA Xu, et al. Ontology driven decision support for the diagnosis of mild cognitive impairment[J]. Computer Methods and Programs in Biomedicine, 2014, 113(3): 781-791. doi: 10.1016/j.cmpb. 2013.12.023.
BAO Zhen and HE Di. An intrusion detection method based on graph theory[J]. Journal of Shanghai Jiaotong University, 2010, 44(9): 1176-1180.
[13]
MITROKOTSA A and DIMITRAKAKIS C. Intrusion detection in MANET using classification algorithms: The effects of cost and model selection[J]. Ad Hoc Networks, 2013, 11(1): 226-237. doi: 10.1016/j.adhoc.2012.05.006.
YAN Weimin and CHEN Wenbo. Data Structure and Application Algorithm Tutorial(Revised Edition)[M]. Beijing: Tsinghua University Press, 2011: 201-202.
[15]
TAKIGUCHI J, LWAMA K, KOZAKI M, et al. A study of autonomous mobile system in outdoor environment[J]. IFAC Computer Aided Control Systems, 1997, 30(4): 61-66. doi: 10.1016/S1474-6670(17)43613-5.
REN Zhuoming, SHAO Feng, LIU Jianguo, et al. Node importance measurement based on the degree and clustering coefficient information[J]. Acta Physica Sinica, 2013, 62(12): 522-526. doi: 10.7498/aps.62.128901.
[18]
ZHANG Xiaowei, HU Bin, MA Xu, et al. Resting-State whole-brain functional connectivity networks for MCI classification using L2-Regularized logistic regression[J]. IEEE Transactions on Nanobioscience, 2015, 14(2): 237-247. doi: 10.1109/TNB.2015.2403274.
LI Lingjuan, XU Xiangkai, and WANG Ruchuan. Research of the mobile agent disposal algorithm in MANET IDS[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2006, 26(3): 52-57.
