Classification of Terrorist Attack Types Using Vote Based Classification Model

Abstract

Due to the rapid increase in terrorist activities throughout the world, there is serious intention required to deal with such activities. Classification of terrorist attack types can be very useful for law enforcement agencies in order to take measures. The findings and classification of terrorist attacks is an obscure task which depends upon various factors. There are different categories of attack types like bombing, assassination, kidnapping, hijacking, infrastructure attack etc. This research focuses on classification of attack types that what kind of attack is happen. The objective of this research is to propose a framework that can classify attack types which can help security agencies and authorities. The proposed framework has four steps. First step is to gather data. We have gathered global terrorism database which contain terrorism records from 1970 to 2012 throughout the world. After acquiring data, proper pre-processing of data is done. After pre-processing of data we have applied individual classification algorithms on dataset as well as we have proposed a vote based classification model which consists of some existing classifiers including K Nearest Neighbor, Naïve Bayes and Decision Tree. The purposed technique achieves the satisfied level of accuracy. Results reveal the improvement in accuracy than the individual classifiers used.