An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets over Data Streams

Yi Min Mao; Xiao Fang Xue; Jin Qing Chen

doi:10.4028/www.scientific.net/AMM.339.341

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 339An Intrusion Detection Model Based on Mining...

An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets over Data Streams

Abstract:

Ming association rules have been proved as an important method to detect intrusions. To improve response speed and detecting precision in the current intrusion detection system, this papers proposes an intrusion detection system model of MMFIID-DS. Firstly, to improve response speed of the system by greatly reducing search space, various pruning strategies are proposed to mine the maximal frequent itemsets on trained normal data set, abnormal data set and current data streams to establish normal and abnormal behavior pattern as well as user behavior pattern of the system. Besides, to improve detection precision of the system, misuse detection and anomaly detection techniques are combined. Both theoretical and experimental results indicate that the MMFIID-DS intrusion detection system is fairly sound in performance.

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Periodical:

Applied Mechanics and Materials (Volume 339)

Pages:

341-348

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.339.341

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Online since:

July 2013

Authors:

Yi Min Mao, Xiao Fang Xue, Jin Qing Chen

Keywords:

Anomaly Detection, Data Streams, Maximal Frequent Itemsets, Misuse Detection

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References

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