Abstract
Radicalism is becoming an increasingly potential concern. Intruder groups are using handful tactics and radicalism has disparaging effects, particularly in Gulf and Pakistan region. Forecasting the pattern of attacks is a complex task. This research paper presents new insights on intruder groups and targets using data mining algorithms. We propose a framework, which uses historical data to train machine-learning classifiers and can predict intruder groups and attack types based on selected features. We analyzed that the major victims of intruder groups would be citizen and property, government, police, and military sectors. We figured out that J48 and IBK learning algorithms perform consistently well under various experimental settings.