SURVEY ON AIS BASED COMPUTER SECURITY SYSTEM

  • Authors

    • Ms S Monica Catherine
    • Soumik Mondal
    https://doi.org/10.14419/ijet.v7i4.6.28667
  • Artificial Immune System(AIS), Danger Theory, Negative Selection Algorithm

  • Abstract

          Artificial intelligence is gaining popularity in wide area of technology. Now days due to increase in computerized system of every field, security of any computer system is a big concern. In recent days, the artificial intelligence is emerging in computer security field, which evolves the computer security in a new way. In the last few years artificial immune system has been applied in various fields. It is a very effective technology to detect anomalous behaviour in the computer system, especially in computer’s security domain. Artificial Immune System is a new bio-inspired model, which grab the attention of researchers to solve various problems in Information Security field. The unique feature of AIS is very promising to solve various issues in security field. A survey on current existing security technique based on Artificial Immune System is presented in this paper.

     

     

  • References

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      [4] He, Y., Yiwen, L., Tao, L. and Bo, M., 2009, November. A method inspired from differential coefficient for calculating danger signals in Artificial Immune System. In Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on (Vol. 1, pp. 429-432). IEEE.

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      [11] https://en.wikipedia.org/wiki/Artificial_immune_system

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      [17] Yin, M., Zhang, T. and Shu, Y., 2012, August. An artificial immune model with danger theory based on changes. In Computer Science & Service System (CSSS), 2012 International Conference on (pp. 672-676). IEEE.

      [18] Aickelin, U., Bentley, P., Cayzer, S., Kim, J. and McLeod, J., 2003, September. Danger theory: The link between AIS and IDS?. In International Conference on Artificial Immune Systems (pp. 147-155). Springer, Berlin, Heidelberg.

      [19] Pereira, G., 2011. Artificial Immune System Algorithm based on Danger Theory.

      [20] Hofmeyr, S.A. and Forrest, S., 2000. Architecture for an artificial immune system. Evolutionary computation, 8(4), pp.443-473.

      [21] Fernandes, D.A., Freire, M.M., Fazendeiro, P.A. and Inácio, P.R., 2017. Applications of artificial immune systems to computer security: A survey. Journal of Information Security and Applications, 35, pp.138-159.

      [22] Hosseinpour, F., Bakar, K.A., Hardoroudi, A.H. and Kazazi, N., 2010, November. Survey on artificial immune system as a bio-inspired technique for anomaly based intrusion detection systems. In Intelligent Networking and Collaborative Systems (INCOS), 2010 2nd International Conference on (pp. 323-324). IEEE.

      [23] Ou, C.M., Wang, Y.T. and Ou, C.R., 2011, June. Intrusion detection systems adapted from agent-based artificial immune systems. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on (pp. 115-122). IEEE.

      [24] Khannous, A., Rghioui, A., Elouaai, F. and Bouhorma, M., 2014, May. Manet security: An intrusion detection system based on the combination of negative selection and danger theory concepts. In Next Generation Networks and Services (NGNS), 2014 Fifth International Conference on (pp. 88-91). IEEE.

      [25] Zainal, K. and Jali, M.Z., 2015. A perception model of spam risk assessment inspired by danger theory of artificial immune systems. Procedia Computer Science, 59, pp.152-161.

      [26] Kim, J., Greensmith, J., Twycross, J. and Aickelin, U., 2010. Malicious code execution detection and response immune system inspired by the danger theory. arXiv preprint arXiv:1003.4142.

      [1] Fang, X., Koceja, N., Zhan, J., Dozier, G. and Dipankar, D., 2012, June. An artificial immune system for phishing detection. In Evolutionary Computation (CEC), 2012 IEEE Congress on (pp. 1-7). IEEE.

      [2] Hong, L., 2008, December. Artificial immune system for anomaly detection. In Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on (pp. 340-343). IEEE.

      [3] Ghohroud, N.F. and Hessabi, S., 2017, December. A bio-inspired method for hardware Trojan detection. In Computer Architecture and Digital Systems (CADS), 2017 19th International Symposium on (pp. 1-2). IEEE.

      [4] He, Y., Yiwen, L., Tao, L. and Bo, M., 2009, November. A method inspired from differential coefficient for calculating danger signals in Artificial Immune System. In Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on (Vol. 1, pp. 429-432). IEEE.

      [5] Brahim Belhaouari Samir, B.B.S., 2010. Immune Multi Agent System for Intrusion Prevention and Self healing System Implement a Non-Linear Classification.

      [6] Ou, C.M. and Ou, C.R., 2011, August. Immunity-inspired host-based intrusion detection systems. In Genetic and Evolutionary Computing (ICGEC), 2011 Fifth International Conference on (pp. 283-286). IEEE.

      [7] Samaras, A., Pangalos, G., Ilioudis, C. and Pagkalos, I., 2009, September. Using nature and bio-inspired technologies for building innovative proactive security mechanisms. In Informatics, 2009. PCI'09. 13th Panhellenic Conference on (pp. 7-13). IEEE.

      [8] Aickelin, U. and Cayzer, S., 2008. The danger theory and its application to artificial immune systems. arXiv preprint arXiv:0801.3549.

      [9] Yiwen, L., He, Y., Tao, L. and Changdong, L., 2009, November. A differential coefficient inspired method for malicious software detection. In 2009 Third International Symposium on Intelligent Information Technology Application (pp. 130-133). IEEE.

      [10] Ramadhan, G., Kurniawan, Y. and Kim, C.S., 2016, October. Design of TCP SYN Flood DDoS attack detection using artificial immune systems. In System Engineering and Technology (ICSET), 2016 6th International Conference on (pp. 72-76). IEEE.

      [11] https://en.wikipedia.org/wiki/Artificial_immune_system

      [12] Hosseinpour, F., Bakar, K.A., Hardoroudi, A.H. and Kazazi, N., 2010, November. Survey on artificial immune system as a bio-inspired technique for anomaly based intrusion detection systems. In Intelligent Networking and Collaborative Systems (INCOS), 2010 2nd International Conference on (pp. 323-324). IEEE.

      [13] Aickelin, U. and Dasgupta, D., 2005. Artificial immune systems. In Search methodologies (pp. 375-399). Springer, Boston, MA.

      [14] Anandita, S., Rosmansyah, Y., Dabarsyah, B. and Choi, J.U., 2015, November. Implementation of dendritic cell algorithm as an anomaly detection method for port scanning attack. In Information Technology Systems and Innovation (ICITSI), 2015 International Conference on (pp. 1-6). IEEE.

      [15] Amer, S. and Leonard, J., 2015, December. Danger theory concepts improving malware detection of intrusion detection systems that uses exact graphs. In Computational Science and Computational Intelligence (CSCI), 2015 International Conference on (pp. 232-237). IEEE.

      [16] Peng, L.X. and Chen, T.W., 2014, October. Automated Intrusion Response System Algorithm with Danger Theory. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on (pp. 31-34). IEEE.

      [17] Yin, M., Zhang, T. and Shu, Y., 2012, August. An artificial immune model with danger theory based on changes. In Computer Science & Service System (CSSS), 2012 International Conference on (pp. 672-676). IEEE.

      [18] Aickelin, U., Bentley, P., Cayzer, S., Kim, J. and McLeod, J., 2003, September. Danger theory: The link between AIS and IDS?. In International Conference on Artificial Immune Systems (pp. 147-155). Springer, Berlin, Heidelberg.

      [19] Pereira, G., 2011. Artificial Immune System Algorithm based on Danger Theory.

      [20] Hofmeyr, S.A. and Forrest, S., 2000. Architecture for an artificial immune system. Evolutionary computation, 8(4), pp.443-473.

      [21] Fernandes, D.A., Freire, M.M., Fazendeiro, P.A. and Inácio, P.R., 2017. Applications of artificial immune systems to computer security: A survey. Journal of Information Security and Applications, 35, pp.138-159.

      [22] Hosseinpour, F., Bakar, K.A., Hardoroudi, A.H. and Kazazi, N., 2010, November. Survey on artificial immune system as a bio-inspired technique for anomaly based intrusion detection systems. In Intelligent Networking and Collaborative Systems (INCOS), 2010 2nd International Conference on (pp. 323-324). IEEE.

      [23] Ou, C.M., Wang, Y.T. and Ou, C.R., 2011, June. Intrusion detection systems adapted from agent-based artificial immune systems. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on (pp. 115-122). IEEE.

      [24] Khannous, A., Rghioui, A., Elouaai, F. and Bouhorma, M., 2014, May. Manet security: An intrusion detection system based on the combination of negative selection and danger theory concepts. In Next Generation Networks and Services (NGNS), 2014 Fifth International Conference on (pp. 88-91). IEEE.

      [25] Zainal, K. and Jali, M.Z., 2015. A perception model of spam risk assessment inspired by danger theory of artificial immune systems. Procedia Computer Science, 59, pp.152-161.

      [26] Kim, J., Greensmith, J., Twycross, J. and Aickelin, U., 2010. Malicious code execution detection and response immune system inspired by the danger theory. arXiv preprint arXiv:1003.4142.

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  • How to Cite

    S Monica Catherine, M., & Mondal, S. (2018). SURVEY ON AIS BASED COMPUTER SECURITY SYSTEM. International Journal of Engineering & Technology, 7(4.6), 469-471. https://doi.org/10.14419/ijet.v7i4.6.28667

    Received date: 2019-03-30

    Accepted date: 2019-03-30