| dc.creator | Bothos I., Vlachos V., Kyriazanos D.M., Stamatiou I., Thanos K.G., Tzamalis P., Nikoletseas S., Thomopoulos S.C.A. | en |
| dc.date.accessioned | 2023-01-31T07:39:15Z | |
| dc.date.available | 2023-01-31T07:39:15Z | |
| dc.date.issued | 2021 | |
| dc.identifier | 10.1109/CSR51186.2021.9527994 | |
| dc.identifier.isbn | 9781665402859 | |
| dc.identifier.uri | http://hdl.handle.net/11615/71861 | |
| dc.description.abstract | 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. © 2021 IEEE. | en |
| dc.language.iso | en | en |
| dc.source | Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience, CSR 2021 | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115711830&doi=10.1109%2fCSR51186.2021.9527994&partnerID=40&md5=61ea51ca50b19854b5c289b4d01736b8 | |
| dc.subject | Data mining | en |
| dc.subject | Deep learning | en |
| dc.subject | Risk perception | en |
| dc.subject | Security of data | en |
| dc.subject | Time series analysis | en |
| dc.subject | Cyber security | en |
| dc.subject | Econometric modelling | en |
| dc.subject | Economic perspective | en |
| dc.subject | Theoretical approach | en |
| dc.subject | Time-series data | en |
| dc.subject | Risk assessment | en |
| dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
| dc.title | Modelling cyber-risk in an economic perspective | en |
| dc.type | conferenceItem | en |
