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Towards accurate intrusion detection based on improved clonal selection algorithm

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Abstract

Artificial immune system constructs a dynamic and adaptive information defense system through a function similar to the biological immune system. In order to resist the external invasion of useless and harmful information and ensure the effectiveness and the harmlessness of received information. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we propose an improved clonal selection algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and cloning them. Experimental results show that the improved algorithm achieves very good performance when applied to intrusion detection. And it is shown that the algorithm is better than BP neural network with its 99.5 % accuracy and 0.1 % false positive rate.

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Acknowledgments

Foundation item: This work was funded by the National Natural Science Foundation of China (No.61373134). It was also supported by the Priority Academic Program Development of Jiangsu Higer Education Institutions(PAPD), Jiangsu Key Laboratory of Meteorological Observation and Information Processing (No.KDXS1105) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET).

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Authors and Affiliations

  1. School of Computer and Software, Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China

    Chunyong Yin, Luyu Ma & Lu Feng

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  1. Chunyong Yin
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  2. Luyu Ma
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  3. Lu Feng
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Correspondence to Chunyong Yin.

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Yin, C., Ma, L. & Feng, L. Towards accurate intrusion detection based on improved clonal selection algorithm. Multimed Tools Appl 76, 19397–19410 (2017). https://doi.org/10.1007/s11042-015-3117-0

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  • DOI: https://doi.org/10.1007/s11042-015-3117-0

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