Detection of Sensitive Data to Counter Global Terrorism

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.

Causal Pathway Extraction from Web-Board Documents

“…With regard to the limited literation of the causal pathway extraction from texts, the extracted causal pathways including the explicit mediator representation of our research supports the preliminary causal inference and also makes non-professionals understand an etiological pathway including disease complication through the social media for the compliance to the preventive treatments. Our proposed method of extracting and representing the causal pathways in terms of the explicit mediators even the implicit mediator occurrences from the documents is based on (1) the wrdCoc-pair matching between the wrdCoc pairs on the test corpus and the WCP elements through the sliding window on the test corpus for the causal pathway extraction where each wrdCoc feature is obtained by the wrdCo-expression matching between the wrdCo expressions on the test corpus and the wrdCo expressions on the wrdCo-Concept Table . In addition, the wrdCoc features from the wrdCo-expression matching are based on the v a , w 1,d , and w 2,e terms with complete matching as in [29]. Since the precisions of determining wrdCoc pairs having CErel from the learning corpus and the test corpus are consistent, the causal pathways extracted by the wrdCoc-pair matching are strengthened (where the WCP elements obtained by the correct determination of wrdCoc pairs having Cerel) And (2) applying the transitive closure to obtain TransCEPair for indi-cating the implicit mediators on the correct extracted causal pathways to represent these implicit mediators with the explicit ones from the dynamic template.…”

Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology

“…These solutions involve complex techniques that are out of this paper's scope and remain as future work. Examples include domain experts and neural networks such as in [34]- [36]. In our case, sensitivity detection is subjective to the data owner (DO) policy and requirements.…”