Terrorist Network Mining

Abstract

Terrorist activities worldwide has moved toward the advancement of different high-finished strategies for dissecting psychological militant gatherings and disrupting government systems. Existing examination found that social network analysis (SNA) is a standout amongst the best and persistent technique for countering fear mongering in interpersonal organizations. In this paper our approach is divided in two phases, in Phase 1 we apply shuffled frog leap algorithm (SFLA) for SNA in order to identify potential terrorist node by analyzing twitter real time data analysis and in phase 2 we use the output of phase 1 as input and generate influence of one node in all the others (n-1) node in node n network by applying node influencing metrics. The advantage of using node influencing metrics comparison to other centrality measures for node influence is that, it is independent of network topology. It can upgrade the capacity of SNA for giving element conduct of online interpersonal organizations. This paper plots an assortment of measures and systems utilized as a part of SNA for counter-psychological oppression exercises.

Cite this Article

Poorva Singhania, Akhilesh Tiwari. Terrorist Network Mining. Journal of Communication Engineering & Systems. 2017; 7(2): 42–52p.