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Authors: Mateusz Kozlowski and Bogdan Ksiezopolski

Affiliation: Institute of Computer Science, Maria Curie-Sklodowska University, Akademicka 9, 20-033 Lublin, Poland

Keyword(s): DDoS, Targeted Attacks, Machine Learning, Testing Methods.

Abstract: Distributed Denial of Service (DDoS) is one of the most popular attacks on the Internet. One of the most popular classes of DDoS attacks is the flood-based, which sends huge amounts of packets to the victim host or infrastructure, causing an overload of the system. One of the attack mitigation systems is based on machine learning (ML) methods, which in many cases has a very high accuracy rate (0.95 – 0.99). Unfortunately, most ML models are not resistant against targeted DDoS attacks. In this article, we present the targeted attacks to the DDoS ML-based mitigation models, which have a high accuracy. After this, we propose a new method of testing ML-based models against targeted DDoS attacks.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kozlowski, M. and Ksiezopolski, B. (2021). A New Method of Testing Machine Learning Models of Detection for Targeted DDoS Attacks. In Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-524-1; ISSN 2184-7711, SciTePress, pages 728-733. DOI: 10.5220/0010574507280733

@conference{secrypt21,
author={Mateusz Kozlowski. and Bogdan Ksiezopolski.},
title={A New Method of Testing Machine Learning Models of Detection for Targeted DDoS Attacks},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT},
year={2021},
pages={728-733},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010574507280733},
isbn={978-989-758-524-1},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT
TI - A New Method of Testing Machine Learning Models of Detection for Targeted DDoS Attacks
SN - 978-989-758-524-1
IS - 2184-7711
AU - Kozlowski, M.
AU - Ksiezopolski, B.
PY - 2021
SP - 728
EP - 733
DO - 10.5220/0010574507280733
PB - SciTePress

- Science and Technology Publications, Lda.
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