  Terrorism has become one of the most tedious problems to deal with and a
prominent threat to mankind. To enhance counter-terrorism, several research
works are developing efficient and precise systems, data mining is not an
exception. Immense data is floating in our lives, though the scarce
availability of authentic terrorist attack data in the public domain makes it
complicated to fight terrorism. This manuscript focuses on data mining
classification techniques and discusses the role of United Nations in
counter-terrorism. It analyzes the performance of classifiers such as Lazy
Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for
observing the trends for terrorist attacks around the world. The database for
experiment purpose is created from different public and open access sources for
years 1970-2015 comprising of 156,772 reported attacks causing massive losses
of lives and property. This work enumerates the losses occurred, trends in
attack frequency and places more prone to it, by considering the attack
responsibilities taken as evaluation class.
