Abstract
In order to solve non-real time problem in traditional intrusion detection technologies, this paper proposes an anomaly detection model based on cloud model and danger theory. First using cloud model as a tool to evaluate the diversity factors between test data and the standard data set, then covert it into signal input of DCA to detect abnormality degree of system. Meanwhile, a dendritic cell algorithm based on data segmented detection is proposed in order to raise real-time response of the system. The paper use KDDCUP99 data sets to validate membership of normal data and detection rate of this model. Experimental results show that the model can effectively distinguish between normal data and abnormal data, and also improve the system anomaly detection capabilities.
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References
Forrest, S., Perelson, A.S., Allen, L., et al.: Self-nonself discrimination in a computer. In: Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy 1994, pp. 202–212. IEEE (1994)
Aickelin, U., Bentley, P.J., Cayzer, S., Kim, J., McLeod, J.: Danger theory: the link between AIS and IDS? In: Bentley, P.J., Hart, E., Timmis, J. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 147–155. Springer, Heidelberg (2003)
Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)
Weiwei, Z., Deyi, L.: Intrusion detection using cloud mode. Comput. Eng. Appl. 39(26), 158–160 (2003)
Lowry, C.A., Woodall, W.H., Champ, C.W., et al.: A multivariate exponentially weighted moving average control chart. Technometrics 34(1), 46–53 (1992)
Yang, H., Dong, H., Liang, Y., et al.: Definition of danger signal in artificial immune system using cloud method. Comput. Eng. Appl. 42(10), 34–45 (2006)
Li, D., Meng, H.: Membership clouds and membership clouds generators. Comput. R&D 32(6), 15–20 (1995)
Gu, F., Greensmith, J., Aickelin, U.: Further exploration of the dendritic cell algorithm: antigen multiplier and time windows. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 142–153. Springer, Heidelberg (2008)
Hettich, S., Bay, S.D.: KDD Cup 1999 Data. http://kdd.ics.uci.edu
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Wang, W., Zhang, C., Zhang, Q. (2014). An Anomaly Detection Model Based on Cloud Model and Danger Theory. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2013. Communications in Computer and Information Science, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43908-1_15
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DOI: https://doi.org/10.1007/978-3-662-43908-1_15
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