Limits...
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.

Show MeSH

Related in: MedlinePlus

The Noordin Muhammad network.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4127216&req=5

fig7: The Noordin Muhammad network.

Mentions: The first case study is taken from [24] titled as Noordin Muhammad network. This subset of the Noordin Top Terrorist Network was drawn primarily from “Terrorism in Indonesia: Noordin's Networks,” a 2006 publication of the International Crisis Group. It includes relational data on the 79 individuals listed in Appendix C of that publication. The data were initially coded by Naval Postgraduate School students as part of the course “Tracking and Disrupting Dark Networks” under the direction of Professor Sean Everton, Codirector of the CORE Lab, and Professor Nancy Roberts. CORE Lab Research Associate Dan Cunningham also reviewed and helped clean the data. Figure 7 shows network generated in NodeXL for Noordin's 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)

The Noordin Muhammad network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: The Noordin Muhammad network.
Mentions: The first case study is taken from [24] titled as Noordin Muhammad network. This subset of the Noordin Top Terrorist Network was drawn primarily from “Terrorism in Indonesia: Noordin's Networks,” a 2006 publication of the International Crisis Group. It includes relational data on the 79 individuals listed in Appendix C of that publication. The data were initially coded by Naval Postgraduate School students as part of the course “Tracking and Disrupting Dark Networks” under the direction of Professor Sean Everton, Codirector of the CORE Lab, and Professor Nancy Roberts. CORE Lab Research Associate Dan Cunningham also reviewed and helped clean the data. Figure 7 shows network generated in NodeXL for Noordin's 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