Ioannis Bothos, Vasileios Vlachos, D. Kyriazanos, I. Stamatiou, K. Thanos, Pantelis Tzamalis, Sotirios E. Nikoletseas, S. Thomopoulos
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In this paper, we present a theoretical approach concerning the econometric modelling for the estimation of cyber-security risk, with the use of time-series analysis methods and alternatively with Machine Learning (ML) based, deep learning methodology. Also we present work performed in the framework of SAINT H2020 Project [1], concerning innovative data mining techniques, based on automated web scrapping, for the retrieving of the relevant time-series data. We conclude with a review of emerging challenges in cyber-risk assessment brought by the rapid development of adversarial AI.
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