Terrorism Analysis and Prediction

Terrorist attacks are the challenging issue across the world and need the attention of the practitioners to cope up deliberately. Predicting the responsible group of an event is a complicated task due to the lack of in depth terrorist historical data. Data mining classification techniques are largely used to resolve the problem. This research proposes a novel ensemble framework for the classification and prediction of the terrorist group that consists of four base classifiers namely; naïve bayes (NB), K nearest neighbour (KNN), Iterative Dichotomiser 3 (ID3) and decision stump (DS). Majority vote based ensemble technique is used to combine these classifiers. The results of individual base classifiers are compared with the majority vote classifier and it is determined through experiments that our approach achieves a considerably better level of accuracy and less classification error rate as compared to the individual classifiers.

National Security has become a big challenge of the 21st century. Undetected suspicious individuals, hiding among normal population, disguising themselves as normal people yet maybe involved in terrorist activities silently. Such individuals are called sleepers and from sleeping terrorist cells. It is a very difficult task to identify such individuals. Two or more individuals, who want to have an interaction to participate in a suspicious activity, may not have a direct link rather they may be use other individuals and different modes of association to establish a link between them. The aim of this paper is to propose a novel idea to identify such individuals/group with different association between members which may not be observed under normal circumstances.

National security is one of the biggest challenges of the world today because of the number of terrorist incidents occurring across the world. It has been seen that suspicious individuals work in very organized groups. They hide themselves between common public and communicate through different media. The whole network is often organized in such a way that all members may not be directly interacting; rather they may be interacting through different mediators and may be using different media for interaction. This type of group is difficult to identify completely because of the presence of mediators and also different media of communication. In order to mitigate the potential risk of terrorist activities, such organized groups are required to be destabilized which is a big challenge for national security organizations. In this paper we propose a new model for detecting such networks. Detection is done by integrating databases having records of associations of suspicious individuals from different domains.

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network.

Related Publication:

Butt, W. H., Akram, M. U., Khan, S. A., & Javed, M. Y. (2014). Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier. The Scientific World Journal, 2014.