Abstract

In the present scenario terrorist attacks are biggest problem for the mankind and whole world is under constant threat from these well-planned, sophisticated and coordinated terrorist operations. Now every country is focusing on counter-terrorism. Counter-terrorism is the practices, tactics, strategies and techniques that governments, militaries, police and security agency uses to prevent or in response to terrorist threats. This paper will focus on terrorist group prediction techniques for counter terrorism. Also analysis of predicting the responsible group using CLOPE algorithm.

Highlights

  • Prediction of terrorist group using historical data of attacks has been less explored due to the lack of detailed terrorist data which contain terrorist group’s attacks and activities

  • Current terrorism informatics, which aims to help security officials using data mining techniques, is mainly focused on using social network analysis (SNA) for structural and positional analysis of terrorist networks where required information is provided from noncrime data [4,5,6,7]

  • The use of data mining technologies in counter terrorism has been flourishing since the U.S Government encouraged the use of information technologies [9]

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Summary

Introduction

Prediction of terrorist group using historical data of attacks has been less explored due to the lack of detailed terrorist data which contain terrorist group’s attacks and activities. The reasons may be its confidentiality & sensitivity. Intelligence agencies are having large amount of data. They are not having enough trained officers to process bulk data in very less time period for the purpose of decision-making about terrorist attacks. In counter terrorism first step after any incident/attack is to find the group names that were involved and to make strategy to catch them

Data Mining for Counter Terrorism
Group Prediction Technique
Dataset and Collection Methodology
Characteristics of the GTD
Definition of Terrorism
Selection of the Clustering Algorithm
Implementation of CLOPE Algorithm
Conclusion
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