An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs

IEEE Access

Al-Sakib Khan Pathan¹  Basem Alkazemi²  Kayhan Zrar Ghafoor³  Ihsan Ali⁴  Noraziah Ahmed⁵  Seyedali Mirjalili⁶  Ali Safaa Sadiq⁷  Suleman Khan⁷ 

[1] Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh;Department of Computer Science, College of Computer & Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia;Department of Computer Science, Faculty of Science, Cihan University-Erbil, Erbil, Iraq;Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia;Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan, Malaysia;Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia;School of Information Technology, Monash University, Bandar Sunway, Malaysia;

关键词: Intrusion detection;    feature extraction;    optimization;    security;    network flow analysis;    computational intelligence;   

DOI  :  10.1109/ACCESS.2018.2835166

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown