Introduction to algorithms
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Survey and taxonomy of feature selection algorithms in intrusion detection system
Inscrypt'06 Proceedings of the Second SKLOIS conference on Information Security and Cryptology
Towards a theory of intrusion detection
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Towards an information-theoretic framework for analyzing intrusion detection systems
ESORICS'06 Proceedings of the 11th European conference on Research in Computer Security
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In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection CFS and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP'99 dataset were also tested. Experiments show that the authors' method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.