Pattern Recognition Letters - Special issue on genetic algorithms
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to intrusion detection
Crossroads - Special issue on computer security
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Using Text Categorization Techniques for Intrusion Detection
Proceedings of the 11th USENIX Security Symposium
Genetic algorithm optimized feature transformation: a comparison with different classifiers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Traffic Data Preparation for a Hybrid Network IDS
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Toward lightweight intrusion detection system through simultaneous intrinsic model identification
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
Incorporating temporal constraints in the planning task of a hybrid intelligent IDS
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Measuring stability of feature ranking techniques: a noise-based approach
International Journal of Business Intelligence and Data Mining
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Feature selection is an important part of the information processing and system development process. The selection of an appropriate set of features can provide an insight into the underlying processes present in the data set and greatly improve the accuracy of the overall classification model. In this paper we investigate the use of a hybrid genetic algorithm/k-nearest neighbour approach to features selection and apply this approach to an intrusion detection data set. We have found that this feature selection process is able to identify features that are important for identifying different types of attacks present in the, data set leading to improved classification accuracy.