Abstract:
Among the issues directly affecting people, terrorism is a high priority global issue.
Adverse effects of the terrorist attacks include huge loss of lives, victims becoming
disable due to injuries, destruction of the properties, deep fear, insecurity and other
mental diseases. Therefore, all the attempts to stop or avoid an attack should be taken.
In that case, identifying possible future attacks in advance holds a significance. Most
research utilizes Machine Learning approaches to ascertain perpetrator, attack type
and attack behaviour using the GTD. But recognizing exact date and country for future
attacks is not feasible in those solutions. In this work, attacks are grouped based on the
date and then the time series algorithm is applied to forecast 365 days. Fbprophet
algorithm, which determines the number of attacks that can happen for each day for
365 days, is used. The output is categorized as attacks or non-attacks. For the
prediction of the perpetrator, Random Forest classifier is used. The main data set was
derived from GTD adhering to its definition of terrorism. According to this research,
authorities can be informed prior to an attack thereby strengthening the national
security. The proposed model in this paper is easily understandable, feasible, effective
and has a better performance. The general impressions of the proposed system from
domain and technical experts were positive. A few Machine Learning experts
suggested improvements for the model which are stated as future enhancements
