Forecasting Cyber-Attacks to Destination Ports Using Machine Learning

Kostas Loumponias, Sotiris Raptis, Eleni Darra, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris

2023

Abstract

To anticipate and counter cyber-attacks that may threaten the stability of the economy, society, and governments around the world, significant efforts have made particularly towards the detection of cyber-attacks, while fewer studies have focused on their forecasting. This paper proposes a framework that provides forecasts of upcoming (within the next minute) cyber-attacks, as well as their type, to a specific destination port. To this end, several machine learning-based methods are applied on measurements (observations) obtained from the network traffic flow. The proposed method is supported by two major pillars: first, the selection of appropriate features generated by the network traffic and, second, in addition to the selected features, the detection of the type of cyber-attacks that occurred in the past. The proposed framework is evaluated on the CIC-IDS2017 synthetic dataset and provides a robust performance in forecasting the type of upcoming cyber-attack in terms of Accuracy, Precision, Recall, F1-score and confusion matrix.

Paper Citation

in Harvard Style

Loumponias K., Raptis S., Darra E., Tsikrika T., Vrochidis S. and Kompatsiaris I. (2023). Forecasting Cyber-Attacks to Destination Ports Using Machine Learning. In Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-624-8, pages 757-764. DOI: 10.5220/0011891000003405

in Bibtex Style

@conference{icissp23,
author={Kostas Loumponias and Sotiris Raptis and Eleni Darra and Theodora Tsikrika and Stefanos Vrochidis and Ioannis Kompatsiaris},
title={Forecasting Cyber-Attacks to Destination Ports Using Machine Learning},
booktitle={Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2023},
pages={757-764},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011891000003405},
isbn={978-989-758-624-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Forecasting Cyber-Attacks to Destination Ports Using Machine Learning
SN - 978-989-758-624-8
AU - Loumponias K.
AU - Raptis S.
AU - Darra E.
AU - Tsikrika T.
AU - Vrochidis S.
AU - Kompatsiaris I.
PY - 2023
SP - 757
EP - 764
DO - 10.5220/0011891000003405