基于圖論的MANET入侵檢測(cè)方法
doi: 10.11999/JEIT170756
-
2.
(蘭州交通大學(xué)電子與信息工程學(xué)院 蘭州 730070) ②(蘭州大學(xué)信息科學(xué)與工程學(xué)院 蘭州 730000) ③(中國(guó)科學(xué)院近代物理研究所 蘭州 730000)
國(guó)家自然科學(xué)基金(61761027, 61261029, 61662043),蘭州交通大學(xué)青年基金(2016004)
Intrusion Detection Method for MANET Based on Graph Theory
-
2.
(School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070 China)
The National Natural Science Foundation of China (61761027, 61261029, 61662043), The Yong Scholar Fund of Lanzhou Jiaotong University (2016004)
-
摘要: 移動(dòng)Ad hoc網(wǎng)絡(luò)(MANET)易遭受各種安全威脅,入侵檢測(cè)是其安全運(yùn)行的有效保障,已有方法主要關(guān)注特征選擇以及特征權(quán)重,而忽略特征間潛在關(guān)聯(lián)性,針對(duì)此問題該文提出基于圖論的MANET入侵檢測(cè)方法。首先通過對(duì)典型攻擊行為分析,合理選擇9種特征作為節(jié)點(diǎn),依據(jù)歐式距離確定節(jié)點(diǎn)間的邊以構(gòu)建結(jié)構(gòu)圖。其次發(fā)掘節(jié)點(diǎn)(即特征)間關(guān)聯(lián)性,綜合考慮節(jié)點(diǎn)鄰居規(guī)模屬性和節(jié)點(diǎn)鄰居之間的緊密程度屬性,利用圖論所對(duì)應(yīng)的統(tǒng)計(jì)特性度分布和聚集系數(shù)具體實(shí)現(xiàn)兩屬性。最后對(duì)比實(shí)驗(yàn)結(jié)果證明此方法與傳統(tǒng)方法相比平均檢測(cè)率和誤檢率分別提高10.15%、降低1.8%。
-
關(guān)鍵詞:
- 入侵檢測(cè) /
- 移動(dòng)Ad hoc網(wǎng)絡(luò) /
- 圖論 /
- 特征關(guān)聯(lián)性
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.-
Key words:
- Intrusion detection /
- Mobile Ad hoc NETwork (MANET) /
- Graph theory /
- Feature correlation
-
馮濤, 郭顯, 馬建峰, 等. 可證明安全的節(jié)點(diǎn)不相交多路徑源路由協(xié)議[J]. 軟件學(xué)報(bào), 2010, 21(7): 1717-1731. doi: 10.3724/ SP.J.1001.2010.03576. 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. 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. 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. 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. 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. 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. 李洪成, 吳曉平, 嚴(yán)博. 面向MANET異常檢測(cè)的分布式遺傳k-means研究[J].通信學(xué)報(bào), 2015, 36(11): 167-173. doi: 10.11959/j.issn.1000-436x.2015269. 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. 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. 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. 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. 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. 包振, 何迪. 一種基于圖論的入侵檢測(cè)方法[J]. 上海交通大學(xué)學(xué)報(bào), 2010, 44(9): 1176-1180. BAO Zhen and HE Di. An intrusion detection method based on graph theory[J]. Journal of Shanghai Jiaotong University, 2010, 44(9): 1176-1180. 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. 嚴(yán)蔚敏, 陳文博. 數(shù)據(jù)結(jié)構(gòu)及應(yīng)用算法教程(修訂版)[M]. 北京: 清華大學(xué)出版社, 2011: 201-202. YAN Weimin and CHEN Wenbo. Data Structure and Application Algorithm Tutorial(Revised Edition)[M]. Beijing: Tsinghua University Press, 2011: 201-202. 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. 王林, 戴冠中. 復(fù)雜網(wǎng)絡(luò)的度分布研究[J]. 西北工業(yè)大學(xué)學(xué)報(bào), 2006, 24(4): 405-409. WANG Lin and DAI Guanzhong. On degree distribution of complex network[J]. Journal of Northwestern Polytechnical University, 2006, 24(4): 405-409. 任卓明, 邵鳳, 劉建國(guó), 等. 基于度與集聚系數(shù)的網(wǎng)絡(luò)節(jié)點(diǎn)重要性度量方法研究[J]. 物理學(xué)報(bào), 2013, 62(12): 522-526. doi: 10.7498/aps.62.128901. 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. 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. 李玲娟, 徐向凱, 王汝傳. MANET的IDS中移動(dòng)代理部署算法的研究[J]. 南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版), 2006, 26(3): 52-57. 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. -
計(jì)量
- 文章訪問數(shù): 1696
- HTML全文瀏覽量: 207
- PDF下載量: 157
- 被引次數(shù): 0