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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

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


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fig9: Cyber attackers network.

Mentions: This dataset consists of real data that has been created by IT department of our institute during detection and capturing of a hackers group who were intruding in our institute's management information system. The network present in the dataset was created when a hacker was traced on a complaint; all his connections were traced from his communication links and his In/Out data log. The network consists of 30 nodes and 114 edges. Figure 9 shows network generated in NodeXL for cyber 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)

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

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

fig9: Cyber attackers network.
Mentions: This dataset consists of real data that has been created by IT department of our institute during detection and capturing of a hackers group who were intruding in our institute's management information system. The network present in the dataset was created when a hacker was traced on a complaint; all his connections were traced from his communication links and his In/Out data log. The network consists of 30 nodes and 114 edges. Figure 9 shows network generated in NodeXL for cyber 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