전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제54권2호
  • /
  • Pages.76-82
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  • 2017
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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악성코드로부터 빅데이터를 보호하기 위한 이미지 기반의 인공지능 딥러닝 기법

Image-based Artificial Intelligence Deep Learning to Protect the Big Data from Malware

  • 김혜정 (경일대학교 사이버보안학과) ;
  • 윤은준 (경일대학교 사이버보안학과)
  • Kim, Hae Jung (Department of Cyber Security, Kyung-il University) ;
  • Yoon, Eun Jun (Department of Cyber Security, Kyung-il University)
  • 투고 : 2016.11.03
  • 심사 : 2017.01.24
  • 발행 : 2017.02.25
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초록

랜섬웨어를 포함한 악성코드를 빠르게 탐지하여 빅데이터를 보호하기 위해 본 연구에서는 인공지능의 딥러닝으로 학습된 이미지 분석을 통한 악성코드 분석 기법을 제안한다. 우선 악성코드들에서 일반적으로 사용하는 2,400여개 이상의 데이터를 분석하여 인공신경망 Convolutional neural network 으로 학습하고 데이터를 이미지화 하였다. 추상화된 이미지 그래프로 변환하고 부분 그래프를 추출하여 악성코드가 나타내는 집합을 정리하였다. 제안한 논문에서 추출된 부분 집합들 간의 비교 분석을 통해 해당 악성코드들이 얼마나 유사한지를 실험으로 분석하였으며 학습을 통한 방법을 이용하여 빠르게 추출하였다. 실험결과로부터 인공지능의 딥러닝을 이용한 정확한 악성코드 탐지 가능성과 악성코드를 이미지화하여 분류함으로써 더욱 빠르고 정확한 탐지 가능성을 보였다.

Malware, including ransomware to quickly detect, in this study, to provide an analysis method of malicious code through the image analysis that has been learned in the deep learning of artificial intelligence. First, to analyze the 2,400 malware data, and learning in artificial neural network Convolutional neural network and to image data. Extracts subgraphs to convert the graph of abstracted image, summarizes the set represent malware. The experimentally analyzed the malware is not how similar. Using deep learning of artificial intelligence by classifying malware and It shows the possibility of accurate malware detection.

키워드

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