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Security tendency analysis techniques through machine learning algorithms applications in big data environments

빅데이터 환경에서 기계학습 알고리즘 응용을 통한 보안 성향 분석 기법

  • 최도현 (숭실대학교 컴퓨터학과) ;
  • 박중오 (동양미래대학 정보통신공학과)
  • Received : 2015.07.20
  • Accepted : 2015.09.20
  • Published : 2015.09.28

Abstract

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

Keywords

Big data;Machine Learning;Sentiment Analysis;Data Mining;Machine Learning Algorithm

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