• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP


Abstract

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimumblack holeļ¼Œ thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.


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Cite this article

[IEEE Style]
Weipeng Guo, Yonghong Chen, Yiqiao Cai, Tian Wang and Hui Tian, "DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP," KSII Transactions on Internet and Information Systems, vol. 11, no. 11, pp. 5574-5591, 2017. DOI: 10.3837/tiis.2017.11.022

[ACM Style]
Guo, W., Chen, Y., Cai, Y., Wang, T., and Tian, H. 2017. DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP. KSII Transactions on Internet and Information Systems, 11, 11, (2017), 5574-5591. DOI: 10.3837/tiis.2017.11.022