네트워크 데이터 정형화 기법을 통한 데이터 특성 기반 기계학습 모델 성능평가
Performance Evaluation of a Machine Learning Model Based on Data Feature Using Network Data Normalization Technique
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초록
최근 4차 산업 혁명 기술 중 하나인 딥러닝(Deep Learning) 기술은 보안 분야에서는 탐지하기 어려운 네트워크데이터의 숨겨진 의미를 식별하고 공격을 예측하는 데 사용되고 있다. 침입탐지에 사용될 딥러닝 알고리즘을 선택하기 전에 데이터의 속성과 품질 분석이 필요하다. 학습에 사용되는 데이터의 오염여부에 따라 탐지 방법에 영향을 주기 때문이다. 따라서 데이터의 특징을 파악하고 특성을 선정해야 한다. 본 논문에서는 네트워크 데이터 셋을 이용하여악성코드의 단계적 특징을 분석하고 특성을 추출하여 딥러닝 모델을 적용하였을 때 각 특성이 성능에 미치는 영향을분석하였다. 네트워크 특징에 따른 특성들의 비교에 대한 트래픽 분류 실험을 진행하였으며 선정한 특성을 기반으로96.52% 정확도를 분류하였다.
Abstract
Recently Deep Learning technology, one of the fourth industrial revolution technologies, is used to identify the hiddenmeaning of network data that is difficult to detect in the security arena and to predict attacks. Property and quality analysisof data sources are required before selecting the deep learning algorithm to be used for intrusion detection. This is becauseit affects the detection method depending on the contamination of the data used for learning. Therefore, the characteristics ofthe data should be identified and the characteristics selected. In this paper, the characteristics of malware were analyzedusing network data set and the effect of each feature on performance was analyzed when the deep learning model wasapplied. The traffic classification experiment was conducted on the comparison of characteristics according to networkcharacteristics and 96.52% accuracy was classified based on the selected characteristics.
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