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Covert network analysis for key player detection and event prediction using a hybrid classifier.

Butt WH, Akram MU, Khan SA, Javed MY - ScientificWorldJournal (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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

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.

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Related in: MedlinePlus

September 11 attackers network.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4127216&req=5

fig8: September 11 attackers network.

Mentions: The dataset for second case study was first compiled by Krebs [1] consisting the tragic September 11 attackers network. The overall network consisted of 62 nodes and 150 edges containing all the attackers and their helpers who helped or coordinated in any way to organize the attacks. Muhammad Atta was the leader as confirmed by Ossama Bin Laden in a video tape [1]. The actual 19 hijackers who got crashed are labeled. They are considered important because they are the actual implementers of the attack. Figure 8 shows network generated in NodeXL for September 11 attackers network.


Covert network analysis for key player detection and event prediction using a hybrid classifier.

Butt WH, Akram MU, Khan SA, Javed MY - ScientificWorldJournal (2014)

September 11 attackers network.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127216&req=5

fig8: September 11 attackers network.
Mentions: The dataset for second case study was first compiled by Krebs [1] consisting the tragic September 11 attackers network. The overall network consisted of 62 nodes and 150 edges containing all the attackers and their helpers who helped or coordinated in any way to organize the attacks. Muhammad Atta was the leader as confirmed by Ossama Bin Laden in a video tape [1]. The actual 19 hijackers who got crashed are labeled. They are considered important because they are the actual implementers of the attack. Figure 8 shows network generated in NodeXL for September 11 attackers network.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

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

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.

Show MeSH
Related in: MedlinePlus