Publication Details
Windower: Feature Extraction for Real-Time DDoS Detection Using Machine Learning
network intrusion detection, NIDS, DDoS mitigation, real-time, stream data
mining, machine learning
Distributed Denial of Service (DDoS) attacks are an ever-increasing type of
security incident on modern computer networks. To tackle the issue, we propose
Windower, a feature-extraction method for real-time network-based intrusion
(particularly DDoS) detection. Our stream data mining module employs a sliding
window principle to compute statistical information directly from network
packets. Furthermore, we summarize several such windows and compute inter-window
statistics to increase detection reliability. Summarized statistics are then fed
into an ML-based attack discriminator. If an attack is recognized, we drop the
consequent attacking source's traffic using simple ACL rules. The experimental
results evaluated on several datasets indicate the ability to reliably detect an
ongoing attack within the first six seconds of its start and mitigate 99% of
flood and 92% of slow attacks while maintaining false positives below 1%. In
contrast to state-of-the-art, our approach provides greater flexibility by
achieving high detection performance and low resources as flow-based systems
while offering prompt attack detection known from packet-based solutions.
Windower thus brings an appealing trade-off between attack detection performance,
detection delay, and computing resources suitable for real-world deployments.
@inproceedings{BUT188949,
author="Patrik {Goldschmidt} and Jan {Kučera}",
title="Windower: Feature Extraction for Real-Time DDoS Detection Using Machine Learning",
booktitle="NOMS 2024-2024 IEEE Network Operations and Management Symposium",
year="2024",
pages="1--10",
publisher="Institute of Electrical and Electronics Engineers",
address="Seoul",
doi="10.1109/NOMS59830.2024.10575699",
isbn="979-8-3503-2793-9",
url="https://www.fit.vut.cz/research/publication/13084/"
}