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

• A modified version of the cuttlefish algorithm is discussed.
• The proposed model can be used as a novel feature-selection model.
• Cuttlefish algorithm is used as a search strategy to find optimal subset of features.
• Decision tree is used to evaluate the quality of the selected features.
• Data pre-processing for feature selection is also examined in the paper.

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.