Attack type prediction using hybrid classifier

Abstract

Due to the rapid increase in terrorist activities throughout the world, there is serious intention required to deal with such activities. There must be a mechanism that can predict what kind of “attack types” can happen in future and important measures can be taken out accordingly. In this paper, a hybrid classifier is proposed which consists of some existing classifiers including K Nearest Neighbor, Naïve Bayes, Decision Tree, Averaged One Dependence Estimators and BIFReader. The proposed technique is implemented in Rapid Miner 5.3 and it achieves the satisfied level of accuracy. Results reveal the improvement in accuracy for the proposed technique as compare to the individual classifiers used.