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The Scientific World Journal
Volume 2014 (2014), Article ID 615431, 13 pages
http://dx.doi.org/10.1155/2014/615431
Research Article

Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan

Received 2 April 2014; Revised 20 June 2014; Accepted 25 June 2014; Published 20 July 2014

Academic Editor: Christian Baumgartner

Copyright © 2014 Wasi Haider Butt et al. 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

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network.