Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Processing of massive audit data streams for real-time anomaly intrusion detection
Computer Communications
Journal of Network and Computer Applications
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Topology preserving SOM with transductive confidence machine
DS'10 Proceedings of the 13th international conference on Discovery science
Mobile botnet detection using network forensics
FIS'10 Proceedings of the Third future internet conference on Future internet
Design and analysis of genetic fuzzy systems for intrusion detection in computer networks
Expert Systems with Applications: An International Journal
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
Resource awareness in computational intelligence
International Journal of Advanced Intelligence Paradigms
Computational intelligence for network intrusion detection: recent contributions
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A survey of anomaly intrusion detection techniques
Journal of Computing Sciences in Colleges
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There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from the models. Among several soft computing techniques neural network and fuzzy logic are incorporated into the system to achieve robustness and flexibility. The self-organizing map (SOM) determines the optimal measures of audit data and reduces them into appropriate size for efficient modeling by HMM. Based on several models with different measures, fuzzy logic makes the final decision of whether current behavior is abnormal or not. Experimental results with some real audit data show that the proposed fusion produces a viable intrusion detection system. Fuzzy rules that utilize the models based on the measures of system call, file access, and the combination of them produce more reliable performance.