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An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons

Ghanem, Waheed Ali H. M. and Aman, Jantan and Ahmed Ghaleb, Sanaa Abduljabbar and Naseer, Abdullah B. (2020) An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons. IEEE Access, 8 (9141282). pp. 130452-130475. ISSN 2169-3536

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Abstract

One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Intrusion detection system (IDS); Multilayer perceptron (MLP); Metaheuristic algorithm (MA); Artificial bee colony algorithm (ABC); Dragonfly algorithm (DA)
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Institute of Postgraduate Studies
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 10 Nov 2022 03:13
Last Modified: 10 Nov 2022 03:13
URI: http://umpir.ump.edu.my/id/eprint/30046
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