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.

[img] Text
FH03-FIK-21-51439.pdf
Restricted to Registered users only

Download (624kB)

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial Bee Colony (ABC), Dragonfly Algorithm (DA), Intrusion Detecting System (IDS), Multi-objective Optimization (MO), Multilayer Perceptron (MLP), Selection of Feature (SoF).
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Faculty of Informatics & Computing
Depositing User: Fatin Safura
Date Deposited: 17 Jan 2022 07:19
Last Modified: 17 Jan 2022 07:19
URI: http://eprints.unisza.edu.my/id/eprint/4752

Actions (login required)

View Item View Item