사이버공격 융합 동향 분석을 위한 딥러닝 기반 보안 취약점 분석 자동화 메커니즘
Deep Learning-Based Automation Cyber Attack Convergence Trend Analysis Mechanism for Deep Learning-Based Security Vulnerability Analysis
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초록
다양한 기술들이 하나로 융합되어 새로운 기술로 변화되고 있는 현재의 기술사회에서 사회의 변화에 발맞추듯 새로운 사이버공격들이 만들어지고 있다. 특히, 다양한 공격들이 하나로 융합됨으로 인해 기존의 보안 체계만으로 시스템을 보호하는데 어려움이 발생하고 있다. 이와 같은 사이버공격에 대응하기 위해 많은 정보가 생성되고 있다. 하지만, 무분별하게 발생하는 취약점 정보는 관리자에게 불필요한 정보를 제공하여 혼란을 유도할 수 있다. 따라서 본 논문에서는 딥러닝 기반의 언어 학습 모델을 이용하여 문서를 학습하고, 취약점 정보를 추출하여 MITRE ATT&CK 프레임워크에 따라 분류함으로써 관리자에게 구분화된 취약점 정보를 제공하여 새로이 발생하고 있는 사이버공격 융합 기술의 분석을 보조하는 메커니즘을 제안한다.
Abstract
In the current technological society, where various technologies are converged into one and being transformed into new technologies, new cyber attacks are being made just as they keep pace with the changes in society. In particular, due to the convergence of various attacks into one, it is difficult to protect the system with only the existing security system. A lot of information is being generated to respond to such cyber attacks. However, recklessly generated vulnerability information can induce confusion by providing unnecessary information to administrators. Therefore, this paper proposes a mechanism to assist in the analysis of emerging cyberattack convergence technologies by providing differentiated vulnerability information to managers by learning documents using deep learning-based language learning models, extracting vulnerability information and classifying them according to the MITRE ATT&CK framework.
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