Abstract
Artificial Immune System (AIS)-based evolutionary algorithms combine rules and randomness to solve optimization and classification problems. Due to their capability in identifying self and non self samples, they have also gained attention in intrusion detection systems. In this paper, we propose a real-time AIS-based anomoly detection algorithm for intrusion detection. The most important features of the proposed method are its high detection rate, low false alarm, low computational complexity, and real-time response to the incoming samples. We compare our proposed method with several well-known anomaly detection algorithms on various datasets. We demonstrate that the proposed method performs the best among others in terms of false alarm, detection rate and time response.
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References
Masri, W., Podgurski, A.: Application-based anomaly intrusion detection with dynamic information flow analysis. Computers & Security 27, 176–187 (2008)
Seok Lee, K., Woo, Z.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanic and Engineering 194, 3902–3933 (2005)
Zhong, Y., Zhang, L.: An Adaptive Artificial Immune Network for Supervised Classification of Multi-Hyperspectral Remote Sensing Imagery. IEEE transaction on Geoscience and Remote Sensing 50(3), 894–909 (2012)
Delibasis, K.K., Asvestas, P.A., Matsopoulos, G.K., Zoulias, E., Tseleni-Balafouta, S.: Computer-Aided Diagnosis of Thyroid Malignancy Using an Artificial ImmuneSystem Classification Algorithm. IEEE Transactions onInformation Technology in Biomedicine 13(5), 680–686 (2009)
Mohammadi, M., Raahemi, B., Akbari, A., Nassersharif, B., Moeinzadeh, H.: Improving linear discriminant analysis with artificial immune system-based evolutionary algorithms. Information Sciences 189, 219–232 (2012)
Ji, Z., Dasgupta, D.: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Feature selection and classification in multiple class datasets: An application to KDDCup 1999 dataset. Systems with Applications 38(5), 5947–5957 (2011)
Tsai, C., Lin, C.: A triangle area based nearest neighbors approach to intrusion detection. Pattern Recognition 43(1), 222–229 (2010)
Gogoi, P., Bhattacharyya, D.: Anomaly Detection Analysis of Intrusion Data using Supervised & Unsupervised Approach. Journal of Convergence Information Technology 5(1) (February 2010)
Mohammadi, M., Raahemi, B., Akbari, A., Nassersharif, B.: Unsupervised Sample Reduction Using Clustering for Intrusion Detection System, Submitted to Security and Communication Network Journal (under review)
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Mohammadi, M., Akbari, A., Raahemi, B., Nassersharif, B. (2012). A Real Time Anomaly Detection System Based on Probabilistic Artificial Immune Based Algorithm. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_16
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DOI: https://doi.org/10.1007/978-3-642-33757-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33756-7
Online ISBN: 978-3-642-33757-4
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