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학술지 Attack Graph Generation with Machine Learning for Network Security
Cited 3 time in scopus Download 79 time Share share facebook twitter linkedin kakaostory
구기종, 문대성, 허준호, 정세훈, 이한성
Electronics, v.11 no.9, pp.1-25
20HR1300, 능동적 사전보안을 위한 사이버 자가변이 기술개발, 문대성
Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the attack graph, which is created based on the vulnerability database, using machine learning and deep learning. Second, it generates the attack graph using network topology and system information with a machine learning model that is trained with the attack graph generated from the vulnerability database. We construct the dataset for attack graph generation with topological and system information. The attack graph generation problem is recast as a multi-output learning and binary classification problem. It shows attack path detection accuracy of 89.52% in the multi-output learning approach and 80.68% in the binary classification approach using the in-house dataset, respectively.
KSP 제안 키워드
Binary Classification, Classification approach, Classification problems, Cyber threats, Detection accuracy, Internet of thing(IoT), Learning approach, Learning model, Multi-output learning, Network topology, System Information
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