A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Adaptive Neuro-Fuzzy Intrusion Detection Systems
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Flexible neural trees ensemble for stock index modeling
Neurocomputing
Neural Networks
Feature selection using rough-DPSO in anomaly intrusion detection
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Evolutionary flexible neural networks for intrusion detection system
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Predict the tertiary structure of protein with flexible neural tree
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This paper proposes an IDS model based on general and enhanced Flexible Neural Tree (FNT). Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient.