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Authors: George Stergiopoulos 1 ; Michalis Detsis 2 ; Sozon Leventopoulos 2 and Dimitris Gritzalis 2

Affiliations: 1 Dept. of Information & Communication Systems Engineering, University of the Aegean, Samos, Greece ; 2 Dept. of Informatics, Athens University of Economics & Business, Athens, Greece

Keyword(s): Risk Assessment, Risk Management, Artificial Intelligence, Graph Theory.

Abstract: This research paper presents a methodology and corresponding tool that aim to automate decision-making in prioritizing cybersecurity investments by identifying a minimal subset of assets based on their risk exposure, the protection of which would yield maximum risk reduction and cost efficiency. The presented method aims to assist in strategic security planning, offering significant savings while ensuring robust cyber defense mechanisms are in place. To achieve this, we developed an application that identifies and classifies critical assets within ICT networks using supervised machine learning, graph centrality measurements and cascading attack paths. We utilize over 100 randomly generated network models taken from existing companies to build a classifier able to determine ICT critical nodes. We use topological features and dependency risk graphs to simulate potential cyberattack paths.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Stergiopoulos, G.; Detsis, M.; Leventopoulos, S. and Gritzalis, D. (2024). Utilizing Machine Learning for Optimizing Cybersecurity Spending in Critical Infrastructures. In Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-709-2; ISSN 2184-7711, SciTePress, pages 544-551. DOI: 10.5220/0012837300003767

@conference{secrypt24,
author={George Stergiopoulos. and Michalis Detsis. and Sozon Leventopoulos. and Dimitris Gritzalis.},
title={Utilizing Machine Learning for Optimizing Cybersecurity Spending in Critical Infrastructures},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT},
year={2024},
pages={544-551},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012837300003767},
isbn={978-989-758-709-2},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT
TI - Utilizing Machine Learning for Optimizing Cybersecurity Spending in Critical Infrastructures
SN - 978-989-758-709-2
IS - 2184-7711
AU - Stergiopoulos, G.
AU - Detsis, M.
AU - Leventopoulos, S.
AU - Gritzalis, D.
PY - 2024
SP - 544
EP - 551
DO - 10.5220/0012837300003767
PB - SciTePress

- Science and Technology Publications, Lda.
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