One class support vector machine for anomaly detection in the communication network performance data
ELECTROSCIENCE'07 Proceedings of the 5th conference on Applied electromagnetics, wireless and optical communications
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Behavior detection using confidence intervals of hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering
Expert Systems with Applications: An International Journal
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Burst detection from multiple data streams: a network-based approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
A malware detection algorithm based on multi-view fusion
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Decision tree based light weight intrusion detection using a wrapper approach
Expert Systems with Applications: An International Journal
Resource awareness in computational intelligence
International Journal of Advanced Intelligence Paradigms
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.