A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

By A. Eesa, Zeynep Orman, A. Brifcani. Expert systems with applications, 2015

360 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[6] DT KDD-Cup 99 Anomaly detection DR, FP, accuracy, ROC curve N/A Yes de la Hoz et al.”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “For that reason feature selection, which is part of dimensionality reduction, is needed to select the optimal subset of feature that represents the entire dataset (Eesa et al., 2015).”
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018, methodology): “[8] showed that the detection rate can be increased while the FAR can be decreased by using a better traffic feature set.”
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, methodology): “Originally evaluation methods in feature selection are divided into four kinds: filter, wrapper, embedded [10, 14, 18], and hybrid [20, 29].”