전기전자학회논문지 (Journal of IKEEE)

  • 제25권3호
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  • Pages.451-461
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  • 2021
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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PE 파일 분석을 위한 함수 호출 그래프 생성 연구

Generating Call Graph for PE file

  • Kim, DaeYoub (Dept. of Information Security, Suwon University)
  • 투고 : 2021.08.18
  • 심사 : 2021.09.23
  • 발행 : 2021.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

다양한 스마트 기기의 보급으로 인하여 악성코드로 인한 피해를 더욱 심각해지면서 머신러닝 기술을 활용한 악성코드 탐지 기술이 주목 받고 있다. 그러나 코드의 단편적인 특성만을 기반으로 머시러닝의 학습 데이터를 구성할 경우, 이를 회피하는 변종 및 신종 악성코드는 여전히 제작하기 쉽다. 이와 같은 문제를 해결하기 위한 방법으로 악성코드의 함수호출 관계를 학습 데이터로 사용하는 연구가 주목받고 있다. 특히, GNN을 활용하여 그래프의 유사도를 측정함으로써 보다 향상된 악성코드 탐지가 가능할 것으로 예상된다. 본 논문에서는 GNN을 악성코드 탐지에 활용하기 위해 바이너리 코드로부터 함수 호출 그래프를 생성하는 효율적인 방안을 제안한다.

As various smart devices spread and the damage caused by malicious codes becomes more serious, malicious code detection technology using machine learning technology is attracting attention. However, if the training data of machine learning is constructed based on only the fragmentary characteristics of the code, it is still easy to create variants and new malicious codes that avoid it. To solve such a problem, a research using the function call relationship of malicious code as training data is attracting attention. In particular, it is expected that more advanced malware detection will be possible by measuring the similarity of graphs using GNN. This paper proposes an efficient method to generate a function call graph from binary code to utilize GNN for malware detection.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT)(No. NRF-2021R1F1A1062954).

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