An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs

Sadiq, Ali Safaa and Alkazemi, Basem and Mirjalili, Seyedali and Ahmed, Noraziah and Khan, Suleman and Ali, Ihsan and Pathan, Al-Sakib Khan and Ghafoor, Kayhan Zrar (2018) An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs. IEEE Access, 6. pp. 29041-29053. ISSN 2169-3536, DOI

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Sophisticated Intrusion attacks against various types of networks are ever increasing today with the exploitation of modern technologies which often severely affect wireless networks. In order to improve the effectiveness of intrusion detection systems (IDSs), data analysis methods such as data mining and classification methods are often integrated with IDSs. Though, numerous studies have contributed in various ways to improve the utilization of data mining for IDS, effective solution often depends on the network setting where the IDS is deployed. In this paper, we propose an efficient IDS based on hybrid heuristic optimization algorithm which is inspired by magnetic field theory in physics that deals with attraction between particles scattered in the search space. Our developed algorithm works in extracting the most relevant features that can assist in accurately detecting the network attacks. These features are extracted by tagged index values that represent the information gain out of the training course of the classifier to be used as a base for our developed IDS. In order to improve the accuracy of artificial neural network (ANN) classifier, we have integrated our proposed hybrid magnetic optimization algorithm-particle swarm optimization (MOA-PSO) technique. Experimental results show that using our proposed IDS based on hybrid MOA-PSO technique provides more accuracy level compared to the use of ANN based on MOA, PSO and genetic algorithm. Updated KDD CUP data set is formed and used during the training and testing phases, where this data set consists of mixed data traffics between attacks and normal activities. Our results show significant gain in terms of efficiency compared to other alternative mechanisms.

Item Type: Article
Funders: Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia
Additional Information: Ihsan Ali. Ph.D. student. Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
Uncontrolled Keywords: computational intelligence; feature extraction; Intrusion detection; network flow analysis; optimization; security
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Apr 2019 07:41
Last Modified: 08 Apr 2019 07:41

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