Sensitive Data Detection Using NN and KNN from Big Data

Adhikari, Binod Kumar; Zuo, Wan Li; Maharjan, Ramesh; Guo, Lin

doi:10.1007/978-3-030-05063-4_49

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…If we are able to identify criminal activities, as described in [10,11], it will help in controlling the negative impact on the economy, and in fighting terrorist activity at national and international levels. This will also restrict the establishment of relationships among terrorist groups.…”

Section: Five-node Clustermentioning

confidence: 99%

“…Although there are several studies [38,[40][41][42] that implemented Singular Value Decomposition to prove its importance in related fields, we executed SVD with annotation and annotators, Lemmatization, StopWord Remover, and TF-IDF to retrieve criminal information in a distributed computing environment and proved its importance. Furthermore, in [10], the researchers only considered evaluation as a performance measure, while we also considered the efficiency and accuracy of the work in demonstrating its significance.…”

Section: For K = 6 and Numterms = 5000mentioning

confidence: 99%

“…It creates unwanted pressure on governments for the purpose of political or personal benefit. In [10], sensitive data is a term used to describe the data related to murder, criminals, burglaries, explosions, domestic violence, druggists, thieves, and cybercriminals that is present in big data. Normally, terrorists use techniques like bombing, kidnapping, assassination, suicide attacks, plane hijacking, etc.…”

Section: Introductionmentioning

confidence: 99%

“…According to [41], Feng et al proposed an orthogonal tensor SVD method in the field of cybersecurity and cyber forensics, achieving promising results for higher-order big data reduction. In [10], Adhikari et al implemented two different machine learning algorithms-KNN and NN-to identify sensitive data from big data with Hadoop clusters, but they only considered the performance measures for mining the criminal information. Cho and Yoon [42] presented an approach for identifying important words for sentiment classification using set operations and implementing truncated singular value decomposition and low-rank matrix decomposition techniques to retrieve documents in order to resolve issues related to noise removal and synonymy.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Sensitive Data to Counter Global Terrorism

et al. 2019

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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.

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Section: Five-node Clustermentioning

confidence: 99%

Section: For K = 6 and Numterms = 5000mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Sensitive Data to Counter Global Terrorism

et al. 2019

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“…As all these computers have been connected to one another by means of the network, the distributed file system has been identified to be very complex compared to its regular system. Thus, it becomes necessary to ensure rectification of problems that are connected to the network and manage the computer within the cluster [3].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

2020

IJRTE

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Abstract: Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have beenused to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.

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