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Mobile Information Systems
Volume 2015 (2015), Article ID 585432, 13 pages
http://dx.doi.org/10.1155/2015/585432
Research Article

Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

1College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2IBM Research and Development Center, Markham, ON, Canada L3R 9Z7

Received 10 November 2014; Accepted 15 January 2015

Academic Editor: Zahoor Khan

Copyright © 2015 El-Sayed M. El-Alfy and Feras N. Al-Obeidat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.