HMM-based integration of multiple models for intrusion detection

In this paper, a novel intrusion detection system based on hidden Markov model, analyzing with Fast Adaptive Clustering Algorithm, combining the characteristic of dynamic adaption and sniffing from multi-model has been proposed. The proposed detection model combines qualities from all these categori...

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Vydáno v:2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE) Ročník 2; s. V2-137 - V2-140
Hlavní autoři: Chen Xiuqing, Zhang Yongping, Tang Jiutao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2010
Témata:
Adaptation model
anomaly detection
detection rate
Fast Adaptive Clustering Algorithm
hidden Markov model
Hidden Markov models
misuse detection
ISBN:1424465397, 9781424465392
ISSN:2154-7491
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Abstract In this paper, a novel intrusion detection system based on hidden Markov model, analyzing with Fast Adaptive Clustering Algorithm, combining the characteristic of dynamic adaption and sniffing from multi-model has been proposed. The proposed detection model combines qualities from all these categories, anomaly detection and misuse detection. The proposed mechanism not only takes the responsibility to collect and detect all of the desired information on each different stage, but also denotes specific clustering algorithm to indicate the significance of possible influence on each clustered data. All of the clustered data and detected normal/abnormal signals will be transferred to the database of the anomaly detection model for further integrated evaluation on those multiple observing factors based on hidden Markov model algorithm. The experimental results with the KDD Cup99 data sets demonstrate that the proposed IDS mechanism possesses good efficiency and has a high detection rate.
AbstractList In this paper, a novel intrusion detection system based on hidden Markov model, analyzing with Fast Adaptive Clustering Algorithm, combining the characteristic of dynamic adaption and sniffing from multi-model has been proposed. The proposed detection model combines qualities from all these categories, anomaly detection and misuse detection. The proposed mechanism not only takes the responsibility to collect and detect all of the desired information on each different stage, but also denotes specific clustering algorithm to indicate the significance of possible influence on each clustered data. All of the clustered data and detected normal/abnormal signals will be transferred to the database of the anomaly detection model for further integrated evaluation on those multiple observing factors based on hidden Markov model algorithm. The experimental results with the KDD Cup99 data sets demonstrate that the proposed IDS mechanism possesses good efficiency and has a high detection rate.
Author Tang Jiutao
Zhang Yongping
Chen Xiuqing
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  surname: Zhang Yongping
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  surname: Tang Jiutao
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  organization: Sch. of Comput. Sci. & Technol., China Univ. of Min. & Technol., Xuzhou, China
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SubjectTerms Adaptation model
anomaly detection
detection rate
Fast Adaptive Clustering Algorithm
hidden Markov model
Hidden Markov models
misuse detection
Title HMM-based integration of multiple models for intrusion detection
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