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Conference Paper
Attrack Graph Learning with Machine Lerning
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- Authors
- Kijong Koo, Dae-Sung Moon, Se-Hoon Jung, Hansung Lee
- Issue Date
- 2020-11
- Citation
- International Conference on Multimedia Information Technology and Applications (MITA) 2020, pp.97-98
- Language
- English
- Type
- Conference Paper
- Abstract
- In this study, we propose a cheap and simple method for generating the Attack Graph. The proposed approach consists of learning and generating stages. First, it learns the Attack Graphs using machine learning and deep learning, which are created based on the vulnerability database. 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 binary classification problem, and the proposed approach achieves an 80.68% detection accuracy of the attack path.
- KSP Keywords
- Binary Classification, Classification problems, Detection accuracy, Graph Learning, Learning model, Machine Lerning, Network topology, System Information, Vulnerability database, attack graph generation, deep learning(DL)