LI Peng, WANG Ruchuan. Research on Network Malicious Code Immune Based on Imbalanced Support Vector Machines[J]. Chinese Journal of Electronics, 2015, 24(1): 181-186.
Citation: LI Peng, WANG Ruchuan. Research on Network Malicious Code Immune Based on Imbalanced Support Vector Machines[J]. Chinese Journal of Electronics, 2015, 24(1): 181-186.

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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  • M. Mokhtar, R. Bi, J. Timmis and A. E. Tyrrell, "A modified dendritic cell algorithm for on-line error detection in robotic systems", Proc. of IEEE Congress on Evolutionary Computation (IEEE CEC 2009), Trondheim, Norway, pp.2055-2062, 2009.
    S.M. Abdulla and Q. Zakaria, "Devising a biological model to detect polymorphic computer viruses Artificial immune system (AIM): Review", Proc. of 2009 International Conference on Computer Technology and Development (ICCTD 2009), Kota Kinabalu, Malaysia, pp.300-304, 2009.
    J. Kim, P.J. Bentley, et al., "Immune system approaches to intrusion detection-a review", Natural Computing, Vol.6, No.4, pp.413-466, 2007.
    E.H.J.G. Aarntzen, C.G. Figdor, et al., "Dendritic cell vaccination and immune monitoring", Cancer Immunology, Immunotherapy, Vol.57, No.10, pp.1559-1568, 2008.
    Li Peng, Wang Ruchuan and Gao Dehua, "Research on rootkit dynamic detection based on fuzzy pattern recognition and support virtual machine technology", Acta Electronica Sinica, Vol.40, No.1, pp.115-120, 2012. (in Chinese)
    L.K. Luo, H. Peng, Q.S. Zhang and C.D. Lin, "A comparison of strategies for unbalance sample distribution in support vector machine", Proc. of IST IEEE Conference on 2006 Industrial Electronics and Applications Industrial Electronics and Applications, Singapore, pp.1-5, 2006.
    Y. Al-Hammadi, U. Aickelin, et al., "Dca for bot detection", Proc. of IEEE World Congress on Computational Intelligence (WCCI2008), Hong Kong, China, pp.1807-1816, 2008.
    T. Stibor, R. Oates, G. Kendall and J.M. Garibaldi, "Geometrical insights into the dendritic cell algorithm", Proc. of the 11th Annual Conference on Genetic and Evolutionary Computation, Shanghai, China, pp.1275-1282, 2009.
    F. Gu, J. Greensmithb and U. Aickelin, "Theoretical formulation and analysis of the deterministic dendritic cell algorith", Biosystems, Vol.111, No.2, pp.127-135, 2013.
    Q. Takeuchi and S. Akira, "Innate immunity to virus infection", Immunological Reviews, Vol.227, No.1, pp.75-86, 2009.
    F. Sun and X. Jin, "Immune danger theory based quantitative model for network security situation awareness", Application Research of Computers, Vol.28, No.7, pp.2680-2686, 2011.
    J. Zheng, et al., "A survey of artificial immune applications", Artificial Intelligence Review, Vol.34, No.1, pp.19-34, 2010.
    P. Jain and S. Goyal, "An adaptive intrusion prevention system based on immunity", Proc. of International Conference on Advances in Computing, Control, & Telecommunication Technologies, Trivandrum, Kerala, India, pp.759-763, 2009.
    M.A.M. Ali, et al., "A novel malware detection framework based on innate immunity and danger theory", Lecture Notes in Electrical Engineering, Vol.215, No.1, pp.29-34, 2013.
    J. Zeng and T. Li, "A novel computer virus detection method from ideas of immunology", Proc. of International Conference on Multimedia Information Networking and Security, Wuhan, China, pp.412-416, 2009.
    W. Yun and Z.J.X. Huijian, "The application of immune theory to virus detection", Computer Applications and Software, Vol.25, No.9, pp.52-54, 2008.
    Chen Zemao, Shen Changxiang and Wu Xiaoping, "A cryptobased immunization model against malicious code", Computer Science, Vol.82, No.1, pp.288-289, 2008. (in Chinese)
    Peng Lingxi, Xix Dongqing, Fu Yingfang, Xiong Wei and Shen Yuli, "Automated intrusion response system model based on danger theory", Journal on Communications, Vol.33, No.1, pp.136-144, 2012. (in Chinese)
    F.X. Sun, "A danger theory inspired security evaluation paradigm for computer network", Advanced Materials Research, Vol.179, No.1, pp.1333-1337, 2011.
    J. Zhang and Y. Liang, "A novel intrusion detection model based on danger theory", Proc. of 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, Hubei, China, pp.867-871, 2008.
    U. Aickelin, P. Bentley, S. Cayzer, J. Kim and J. McLeod, "Danger theory: The link between AIS and IDS?", Proc. of Second International Conference on Artificial Immune Systems (ICARIS 2003), Edinburgh, UK, pp.147-155, 2003.
    D. Dasgupta, "Advances in artificial immune systems", Computational Intelligence Magazine, Vol.1, No.4, pp.40-49, 2006.
    X. Ma and H. Wu, "Power system short-term load forecasting based on cooperative co-evolutionary immune network model", Proc. of ICETC 2010 The 2nd International Conference on Education Techhnology and Computer, Shanghai, China, pp.582- 585, 2010.
    T. Liu, et al., "Adaptive immune response network model", Proc. of the 5th International Conference on Intelligent Computing (ICIC 2009), Ulsan South Korea, pp.890-898, 2009.
    T. Li, "An immune based model for network monitoring", Chinese Journal of Computers, Vol.29, No.9, pp.1515-1522, 2006. (in Chinese)
    C.C. Chang and C.J. Lin, "LIBSVM: A library for support vector machines", ACM Transactions on Intelligent Systems and Technology (TIST), Vol.2, No.3, pp.27, 2011.
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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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