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Wireless Personal Communications

, Volume 104, Issue 2, pp 595–615 | Cite as

Using Improved Hilbert–Huang Transformation Method to Detect Routing-Layer Reduce of Quality Attack in Wireless Sensor Network

  • Hongsong ChenEmail author
  • Ming Liu
  • Fu Zhongchuan
Article
  • 28 Downloads

Abstract

Denial of Service (DoS) attack is a serious threat to the security of Wireless Sensor Netwokr (WSN). Moreover, Reduce of Quality (RoQ) attack is a special DoS attack type. The RoQ attack is a potential DoS attack, it is simulator to normal traffic,so it is difficult to be detected by traditional detection method. Hilbert–Huang Transform (HHT) time–frequency analysis method can be used to analyze the non-linear signal generated by RoQ attack. However, mode mixing and false component are the challenge problems to analyze the RoQ attack by HHT method. Ensemble empirical mode decomposition (EEMD) method is used to eliminate the mode mixing problem. Correlated coefficient method is used to recognize the false components. In this paper, EEMD and correlated coefficient methods are combined to detect the weak signal generated by RoQ attack. Ad Hoc On-demand Multi-path Distance Vector routing protocol and random Routing REQuest flooding attack are simulated to implement RoQ attack in wireless sensor network by Network Simulator. Suitable white noises are added to original signal to detect the weak RoQ attack signal. Experiment results show that the improved HHT methods are highly effective to detect RoQ attack, when the standard deviation of white noise is 0.01 and the number of ensemble is 80, the detection results is best. When the correlated coefficient of intrinsic mode functions (IMFs) is greater than 0.2, the IMFs can be used to analyze and detect RoQ attack. To our knowledge, this is the first quantitative experiment and method research on the routing-layer RoQ attack detection in WSN.

Keywords

HHT EEMD Correlation coefficient RoQ attack AOMDV 

Notes

Acknowledgements

This work was supported by National Key Research and Development Program of China (2018YFB0803403, 2018YFB0803400). National Social Science Fund of China (18BGJ071). Fundamental Research Funds for the Central Universities (TW201706). Beijing Natural Science Foundation (4142034).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijingChina
  3. 3.Department of Public Security Science and TechnologyBeijing Police CollegeBeijingChina
  4. 4.School of Computer ScienceHarbin Institute of TechnologyHarbinChina

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