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Ensemble Model using Multiple Profiles for Analytical Classification of Threat Intelligence

보안 인텔리전트 유형 분류를 위한 다중 프로파일링 앙상블 모델

  • 김영수 (배재대학교 사이버보안학과)
  • Received : 2016.11.25
  • Accepted : 2016.12.26
  • Published : 2017.03.28

Abstract

Threat intelligences collected from cyber incident sharing system and security events collected from Security Information & Event Management system are analyzed and coped with expanding malicious code rapidly with the advent of big data. Analytical classification of the threat intelligence in cyber incidents requires various features of cyber observable. Therefore it is necessary to improve classification accuracy of the similarity by using multi-profile which is classified as the same features of cyber observables. We propose a multi-profile ensemble model performed similarity analysis on cyber incident of threat intelligence based on both attack types and cyber observables that can enhance the accuracy of the classification. We see a potential improvement of the cyber incident analysis system, which enhance the accuracy of the classification. Implementation of our suggested technique in a computer network offers the ability to classify and detect similar cyber incident of those not detected by other mechanisms.

Keywords

Big Data;Threat Intelligence;Cyber Incident;Profile;Ensemble Model;Machine Learning

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