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Nong, Y., Qiang, C., Borror, C.M.: EWMA forecast of normal system activity for computer intrusion Detection.

In: The First International Conference on Information and Communication Technologies, pp. Murali, A., Rao, M.: A survey on intrusion detection approaches.

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. 3–14 (1995)Ĭhen, Y.L., Chen, S.S., Hsu, P.Y.: Mining hybrid sequential patterns and sequential rules. KeywordsĪgrawal, R., Srikant, R.: Mining sequential patterns. The main advantage of the proposed algorithm is the use of fuzzy genetic approach to discover optimized sequences in the network traffic data to classify and detect intrusion. The paper defines the confidence of the FTI sequences, which is not yet defined in the previous researches. The main objective is to achieve maximum confidence and maximum coverage in the FTI sequential patterns. Our approach uses two measures as objectives, namely: Confidence and Coverage to prune the traditional Apriori algorithm. Fuzzy solves the sharp boundary problem and the refinement ability of GA helps to find the global optimum FTI sequential patterns. In this paper, we contributed to the ongoing research on FTI sequential pattern mining by proposing a multi objective Genetic Algorithm (GA) based method. have proposed a fuzzy time-interval (FTI) sequential pattern mining algorithms, which reveals the time intervals between successive patterns. From these discovered sequential patterns, we can discover the order of the patterns however, they cannot tell us the time intervals between successive patterns. Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data-mining problem with broad applications.