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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

Flow diagram for handling data redundancy.
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Related In: Results  -  Collection


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fig2: Flow diagram for handling data redundancy.

Mentions: First step uses two rank sum tests, that is, Wilcoxon rank-sum and Ansari-Bradley tests. Wilcoxon rank-sum test is a nonparametric test of the hypothesis that two populations are the same against an alternative hypothesis that the two distributions differ only with respect to the median. It has higher efficiency on nonnormal distributions such as a mixture of normal distributions [13]. Ansari-Bradley test compares two independent samples which come from the same distribution against the alternative that they come from the same distributions having the same median and shape but different variances [14]. Preprocessing step also checks for duplicate entries and removes all such entries to avoid redundancy. The last step in preprocessing is to handle missing values in the data. The preprocessing technique identifies the missing feature values and then they are replaced by the mean value for that feature. This procedure is performed for those attributes where values are missing in less than 50% of the instances. If the number of instances with missing values is more than or equal to 50%, the particular attribute is rejected and not used further. Figure 2 shows the flow diagram of data preprocessing to handle any kind of data redundancies.


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

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

Flow diagram for handling data redundancy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Flow diagram for handling data redundancy.
Mentions: First step uses two rank sum tests, that is, Wilcoxon rank-sum and Ansari-Bradley tests. Wilcoxon rank-sum test is a nonparametric test of the hypothesis that two populations are the same against an alternative hypothesis that the two distributions differ only with respect to the median. It has higher efficiency on nonnormal distributions such as a mixture of normal distributions [13]. Ansari-Bradley test compares two independent samples which come from the same distribution against the alternative that they come from the same distributions having the same median and shape but different variances [14]. Preprocessing step also checks for duplicate entries and removes all such entries to avoid redundancy. The last step in preprocessing is to handle missing values in the data. The preprocessing technique identifies the missing feature values and then they are replaced by the mean value for that feature. This procedure is performed for those attributes where values are missing in less than 50% of the instances. If the number of instances with missing values is more than or equal to 50%, the particular attribute is rejected and not used further. Figure 2 shows the flow diagram of data preprocessing to handle any kind of data redundancies.

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