INTELLIGENCE GENERATION USING DATA MINING TECHNIQUES FOR SURGICAL MILITARY OPERATIONS

Abstract

Terrorism has become an ever increasing menace globally, especially in the Indian- subcontinent with its diverse terrain and inherent threats. Age old manual scrutiny of terror attacks by analysts is cumbersome due to the inability of the analyst to concurrently process large amount of data in a reasonable time frame. Moreover, pe- culiar and complex relationships between numerous terror attributes can be unnoticed by human analysts. As “Necessity is the mother of invention”, developing automated tools for generating intelligence becomes inescapable to speed up the efforts of Security Forces (SF) in fighting terrorists. Application of data mining techniques to analyze terrorist attacks, thus, is the need of the hour. The Security Forces (SF) in the Indian- subcontinent 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, this dissertation provides concrete intel- ligence about various terror outfits operating in the most active Jammu and Kashmir (J&K) region of the Indian sub-continent. The equally sensitive and terror hit areas are the north eastern states of the country which have also been analysed for predicting the Modus Operandi of the different outfits existing there. iii Past work on terrorism analysis and terror forecasting models for preventing terror attacks range acrosss all realms of data mining techniques with clustering being the epicentre of entire research work. Immediate analysis of sensitive locations in terms of their proximity to other In data mining, clustering is performed mainly on the historical records which come from numerous geo-spatial-temporal and demographic information sources and even rich and swiftly expanding social media applications that surround events of concern. Though data mining of twitter tweets and facebook posts in the arena of social networking is the in thing but of limited or no use to SF. As SF operate in inhospitable terrain and inclement weather conditions where neither there is any mobile connectivity nor any access to Internet. Thus, providing foot soldiers with accurate analyzed data in terms of Hard Int is of paramount importance. This will not only boost their morale but even save their precious lives which is the ultimate aim of this dissertation.