GenIt-e-MoTo: Generation of intelligence to envisage modus operandi of terror outfits

Abstract

Terrorism is on the rise in the Indian Sub-Continent and application of data mining techniques to analyse terrorist attacks is the need of the hour. The Security Forces (SF) in this region still rely on traditional and manual analysis methods. Data mining in this field is in its budding stage and if utilised efficiently will greatly facilitate the SF in preventing any terrorist attacks. SF are constantly searching for latest data mining techniques to augment terror analytics and improve protection of the local civilians and self thereby reducing collateral damage. Predicting terror attacks can push the potential of SF to the beat of terrorist activities. It is significant to recognise the spatial and temporal patterns for a better learning of terror incidents and to conceive their correlation. Clustering and Association rule mining (ARM) thus become strong contenders for efficient terror strikes forecasting. The above techniques can be used for a systematic profiling of outfits thus leading to the discovery of a unique pattern of operations i.e. Modus Operandi (MO) of a particular terror outfit. After gaining knowledge from data mining it is essential to convert it into actionable intelligence in order to be used by foot soldiers. Therefore, the paper provides concrete intelligence about various terror outfits operating in the most active Jammu and Kashmir (J&K) region of the Indian sub-continent.