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

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.

Related Publication:

Butt, W. H., Akram, M. U., Khan, S. A., & Javed, M. Y. (2014). Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier. The Scientific World Journal, 2014.

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