A Study on the Insider Behavior Analysis Using Machine Learning for Detecting Information Leakage

정보 유출 탐지를 위한 머신 러닝 기반 내부자 행위 분석 연구

  • Received : 2017.05.18
  • Accepted : 2017.06.05
  • Published : 2017.06.30


In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.


Supported by : 광운대학교


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