Intrusion Detection Method for MANET Based on Graph Theory

ZHANG Bingtao; WANG Xiaopeng; WANG Lücheng; ZHANG Zhonglin; LI Yanlin; LIU Hu

doi:10.11999/JEIT170756

Volume 40 Issue 6

May 2018

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Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1446-1452

Bin WANG, Yuesheng HOU. Extraction of Target Propeller Features in Alpha Distribution Noise[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2478-2484. doi: 10.11999/JEIT190916

Citation:

ZHANG Bingtao, WANG Xiaopeng, WANG Lücheng, ZHANG Zhonglin, LI Yanlin, LIU Hu. Intrusion Detection Method for MANET Based on Graph Theory[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1446-1452. doi: 10.11999/JEIT170756

Bin WANG, Yuesheng HOU. Extraction of Target Propeller Features in Alpha Distribution Noise[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2478-2484. doi: 10.11999/JEIT190916

Citation:

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Intrusion Detection Method for MANET Based on Graph Theory

doi: 10.11999/JEIT170756

1.
2.
(School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070 China)

Funds:

The National Natural Science Foundation of China (61761027, 61261029, 61662043), The Yong Scholar Fund of Lanzhou Jiaotong University (2016004)

Received Date: 2017-07-25
Rev Recd Date: 2018-02-28
Publish Date: 2018-06-19

Abstract

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.
- Intrusion detection,
- Mobile Ad hoc NETwork (MANET),
- Graph theory,
- Feature correlation

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References(26)

References

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