Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification

Ghanem, W.A.H.M and El-Ebiary, Y.A.B. and Abdulnab, M. and Tubishat, M. and Alduais, N.A.M. and Nasser, A.B. and Abdullah, N. and Al-wesabi, O.A. (2021) Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification. In: 2nd International Conference on Advances in Cyber Security, 08-09 Dec 2020, Penang, Malaysia.

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Abstract

Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches.

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