LI Peng and WANG Ruchuan, “Research on Network Malicious Code Immune Based on Imbalanced Support Vector Machines,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 181-186, 2015,
Citation: LI Peng and WANG Ruchuan, “Research on Network Malicious Code Immune Based on Imbalanced Support Vector Machines,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 181-186, 2015,

Research on Network Malicious Code Immune Based on Imbalanced Support Vector Machines

Funds:  The work is supported by the National Natural Science Foundation of China (No.61170065, No.61373017, No.61203217), the Natural Science Foundation of Jiangsu Province (No.BK20140888, No.BK20130882), Scientific & Technological Support Project of Jiangsu Province (No.BE2012183, No.BE2012755), Natural Science Key Fund for Colleges and Universities in Jiangsu Province (No.12KJA520002), Scientific Research & Industry Promotion Project for Higher Education Institutions (No.JHB2012-7) and Jiangsu Planned Projects for Postdoctoral Research Funds (No.1302090B)
  • Received Date: 2013-09-01
  • Rev Recd Date: 2014-02-01
  • Publish Date: 2015-01-10
  • The malicious computer code immune system and the biological immune system are highly similar: both preserve the stability of the system in real time in a constantly changing environment. This similarity is exploited to design a malicious code immune system to solve the malware active defense problem. The malicious code immunization project is mainly composed of four major components: the immune information collection program, immune information filtering processing program, immunization information discrimination program, and immune response program. An imbalanced support vector machine method was applied to optimize output results of malicious code immunization, thereby removing uncertain malicious code immune outputs. This demonstrates in detail the feasibility of the imbalanced support vector machine method in optimizing the immunization program output data. We showed that an imbalanced support vector machines can optimize the outputs of the malicious code immune system by removing glitches from the outputs. As a result, the machine helps to determine the precise time of the emergence of the immune response.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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