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  1. Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics (CSI-KDD '09)
  2. Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
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AntiPhish: lessons learnt
Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
Data security and integrity: developments and directions
Towards trusted intelligence information sharing
Addressing the attack attribution problem using knowledge discovery and multi-criteria fuzzy decision-making
Social networks integration and privacy preservation using subgraph generalization
Malware detection using statistical analysis of byte-level file content
Design of a temporal geosocial semantic web for military stabilization and reconstruction operations
Online phishing classification using adversarial data mining and signaling games
On the efficacy of data mining for security applications
A study of online service and information exposure of public companies

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Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection

Content Provider ACM Digital Library
Author Chen, Yi-Ming Chen, Yu-Shu
Abstract Traditional Hidden Markov Model (HMM) has been successfully applied to anomaly intrusion detection. Incremental HMM (IHMM) further improves the training time of HMM. However, both HMM and IHMM still have the problem of high false positive rate. In this paper, we propose an Adaboost-IHMM to combine IHMM and adaboost for anomaly intrusion detection. As adaboost firstly uses many IHMMs to collectively classify samples then decides the results of samples' classifications, the Adaboost-IHMM can improve the accurate rate of classifications. Experimental results with Stide datasets show that the proposed method can significantly improve the false positive rate by 70% without decreasing detection rate. Besides, we also propose a method to adjust the normal profile for avoiding erroneous detection caused by changes of normal behavior. We perform with experiments with realistic datasets extracted from the use of popular browsers. Compared with traditional HMM method, our method can improve the training time by 90% to build a new normal profile.
Starting Page 3
Ending Page 9
Page Count 7
File Format PDF
ISBN 9781605586694
DOI 10.1145/1599272.1599276
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Normal profile Anomaly intrusion detection Ihmm Adaboost
Content Type Text
Resource Type Article
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