Detection of Sensitive Data to Counter Global Terrorism

Binod Kumar Adhikari,Ramesh Maharjan,Xuming Han,Shining Liang,Wanli Zuo

doi:10.3390/app10010182

Binod Kumar Adhikari, Ramesh Maharjan + Show 3 more

Open Access

https://doi.org/10.3390/app10010182

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Abstract

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.

Highlights

Global Terrorism is the use of intentional violence against civilians and neutral militants for the purpose of political, cultural, and social benefits around the world [1]
As our work is related to the Resilient Distributed Datasets (RDD) environment, we found an efficient method to remove
Due to the fact that terrorism creates a negative impact on the economy, establishes relationships with other terrorist groups, and creates potential risks for societies at national and international levels, this paper chose to study methods of identifying sensitive data from big data using a distributed computing environment, annotation, the StopWords Removal technique, Lemmatization, Term Frequency and Inverse Document Frequency, and Singular Value Decomposition

Summary

Introduction

Global Terrorism is the use of intentional violence against civilians and neutral militants for the purpose of political, cultural, and social benefits around the world [1]. The administrative expense of combating global terrorism is too high [3,4]. In the era of big data, the advent of the latest technologies, the use of social media, mobile technologies, and cloud drives has led to the generation of different types of voluminous data in the form of big data. Criminals use all of these platforms to exploit information, harm society, and globally dispute criminal activities, leading to financial loss, cyber war, and cybercrime.

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