텐서플로우를 활용한 GPU 환경에서 정적 악성코드 탐지 기법
Static Malware Detection Mechanism in GPU Environment using TensorFlow
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초록
최근, 개인용 PC 및 다양한 모바일 디바이스에 보안 패치의 취약점을 통해 신종 및 변종 악성코드의 감염이 빠르게 확산되어 개인 정보 유출, 공인 인증서 탈취, 암호화폐 채굴 등의 다양한 피해가 급증되고 있다. 이를 위해 악성코드 시그니처 기반 악성코드 탐지 기법을 이용하고 있지만, 빠르게 생성되는 신·변종 악성코드를 탐지의 정확도가 현저히 낮다. 본 연구에서는 LSTM를 이용하여 알려진 악성코드뿐만 아니라 신·변종 악성코드들을 탐지하는 스킴을 제안한다.
Abstract
Recently, the infection of new and modified malicious codes is rapidly spreading through the vulnerability of security patches on personal PCs and various mobile devices, and various damages such as personal information leakage, certificate stealing, and cryptocurrency mining are rapidly increasing. For this reason, the malware signature-based malware detection technique is used, but the accuracy of detection of newly generated malicious code is remarkably low. In this paper, we propose a scheme for detecting new and modified malicious codes as well as known malicious codes using LSTM.
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