Windower: Feature Extraction for Real-Time DDoS Detection Using Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133620" target="_blank" >RIV/63839172:_____/24:10133620 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10575699" target="_blank" >https://ieeexplore.ieee.org/document/10575699</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/NOMS59830.2024.10575699" target="_blank" >10.1109/NOMS59830.2024.10575699</a>
Alternative languages
Result language
angličtina
Original language name
Windower: Feature Extraction for Real-Time DDoS Detection Using Machine Learning
Original language description
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.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LM2023054" target="_blank" >LM2023054: e-Infrastructure CZ</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PROCEEDINGS OF 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024
ISBN
979-8-3503-2793-9
ISSN
1542-1201
e-ISSN
—
Number of pages
10
Pages from-to
1-10
Publisher name
IEEE
Place of publication
Seoul, Republic of Korea
Event location
Seoul, South Korea
Event date
May 6, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001270140300170