[1]Wang Yaoguang,Chen Weiquan,Wu Zhenbang,et al.Network intrusion detection algorithm based on hybrid differential evolution algorithm[J].Journal of Zhengzhou University (Engineering Science),2017,38(06):29-32.[doi:10.13705/j.issn.1671-6833.2017.06.006]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
38卷
Number of periods:
2017 06
Page number:
29-32
Column:
Public date:
2017-11-20
- Title:
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Network intrusion detection algorithm based on hybrid differential evolution algorithm
- Author(s):
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Wang Yaoguang1; Chen Weiquan1; Wu Zhenbang1; Qin Yong2Huang Han3
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1. Quality Supervision and Testing Center of Dongguan City, Guangdong Province, Dongguan, Guangdong, 523000; 2. School of Computer Science, Dongguan Institute of Technology, Dongguan, Guangdong 523000; 3. Software College, South China University of Technology, Guangzhou, Guangdong 510006
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- Keywords:
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- CLC:
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- DOI:
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10.13705/j.issn.1671-6833.2017.06.006
- Abstract:
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Intrusion detection algorithm based on machine learning method is one of research hotspot in the field of network equiment testing. In the face of the real-worid application requirement, machine learning methods should be further optimized to achieve accurate and stable detection effect. The study optimize steadily several key parameters of least squares support vector machine (SVM) by designing a hybird differential evolution algorithm with disturbance vector and improved the intrusion detection accuracy and stability of least squares support vector machine (SVM) algorithm by means of adaptive parameter turning. The experimental results in KDD Cup 09 test set showed that, the proposed network intrusion detedtion algorithm based on hybird dieeerential evolution algorithm had better performance on average than many similar algorithm at present.