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http://dx.doi.org/10.33778/kcsa.2020.20.3.115

Cyber Threats Prediction model based on Artificial Neural Networks using Quantification of Open Source Intelligence (OSINT)  

Lee, Jongkwan (육군사관학교 컴퓨터학과)
Moon, Minam (육군사관학교 수학과)
Shin, Kyuyong (육군사관학교 컴퓨터학과)
Kang, Sungrok (육군사관학교 심리경영학과)
Publication Information
Convergence Security Journal / v.20, no.3, 2020 , pp. 115-123 More about this Journal
Abstract
Cyber Attack have evolved more and more in recent years. One of the best countermeasure to counter this advanced and sophisticated cyber threat is to predict cyber attacks in advance. It requires a lot of information and effort to predict cyber threats. If we use Open Source Intelligence(OSINT), the core of recent information acquisition, we can predict cyber threats more accurately. In order to predict cyber threats using OSINT, it is necessary to establish a Database(DB) for cyber attacks from OSINT and to select factors that can evaluate cyber threats from the established DB. We are based on previous researches that built a cyber attack DB using data mining and analyzed the importance of core factors among accumulated DG factors by AHP technique. In this research, we present a method for quantifying cyber threats and propose a cyber threats prediction model based on artificial neural networks.
Keywords
Open Source Intelligence(OSINT); Artificial Neural Network; Cyber Threats; Prediction model;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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