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

Eesa A. S., Orman Z., BRIFCANI A. M. A.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.42, sa.5, ss.2670-2679, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.eswa.2014.11.009
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2670-2679
  • İstanbul Üniversitesi Adresli: Evet

Özet

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. (C) 2014 Elsevier Ltd. All rights reserved.