Detection of Sensitive Data to Counter Global Terrorism

Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale.