Abstract
This paper proposes a combined approach of Negative Selection Algorithm and Artificial Immune Network for virus detection. The approach contains the following stages: the first stage is data extraction and clustering. In the second stage, the negative selection algorithm is deployed to create the first generation of detectors. In the third stage, aiNet is used to improve detectors’ coverage and enhance the ability of detecting unknown viruses. Finally, generated detectors are used to computing danger level of files and a classifier is used to train them and test performance of the system. The experimental results show that in the suitable conditions, the proposed approach can achieve reasonably high virus detection rate.
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References
Al-Enezi, J., Abbod, M., AI-Sharhan, S.: Artificial Immune Systems - Models, Algorithms and Applications. International Journal of Research and Reviews in Applied Sciences 3(2), 118–131 (2010)
Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-Nonself Discrimination in a Computer. In: Research in Security and Privacy, pp. 202–212. IEEE, Oakland (1994)
Gong, M., Zhang, J., Ma, J., Jiao, L.: An efficient negative selection algorithm with further training for anomaly detection. Knowledge-Based Systems 30, 185–191 (2012)
Sahu, A., Maharana, P.: Negative Selection Method for Virus Detection in a Cloud. International Journal of Computer Science and Information Technologies 4, 771–774 (2013)
de Castro, L., Von Zuben, F.: An evolutionary immune network for data clustering. In: The IEEE SBRN (Brazilian Symposium on Artificial Neural Networks), pp. 84–89. Rio de Janeiro (2000)
Jerne, N., Cocteau, J.: Idiotypic Networks and Other Preconceived Ideas. Immunological Reviews, 5–24 (1984)
Jerne, N.: Towards a network theory of the immune system. Ann Immunol (Paris), 373–389 (1974)
Chao, R., Tan, Y.: A Virus Detection System Based on Artificial Immune System. In: International Conference on Computational Intelligence & Security, vol. 1, pp. 6–10 (2009)
de Castro, L., Von Zuben, F.: Learning and optimization using the clonal selection principle. In: Evolutionary Computation, pp. 239–251. IEEE (2002)
Rassam, M., Maarof: Artificial Immune Network Clustering approach for Anomaly Intrusion Detection. Journal of Advances in Information Technology 3, 147–154 (2012)
Wang, X., Hua, J., Deng, Z.: A Controllable and Adaptable Computer Virus Detection Model. In: Fifth International Joint Conference on INC, IMS and IDC, Seoul, pp. 1977–1981 (2009)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3), 273–297 (1995)
Vert, J.-P., Tsuda, K., Schölkopf, B.: A primer on kernel methods. Kernel Methods in Computational Biology, 5–70 (2004)
Chang, Y.-W., Hsieh, C.-J., Chang, K.-W., Ringgaard, M., Lin, C.-J.: Training and testing low-degree polynomial data mappings via linear SVM. J. Machine Learning Research 11, 1471–1490 (2010)
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Nguyen, V.T., Nguyen, T.T., Mai, K.T., Le, T.D. (2014). A Combination of Negative Selection Algorithm and Artificial Immune Network for Virus Detection. In: Dang, T.K., Wagner, R., Neuhold, E., Takizawa, M., Küng, J., Thoai, N. (eds) Future Data and Security Engineering. FDSE 2014. Lecture Notes in Computer Science, vol 8860. Springer, Cham. https://doi.org/10.1007/978-3-319-12778-1_8
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DOI: https://doi.org/10.1007/978-3-319-12778-1_8
Publisher Name: Springer, Cham
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