Experiments with Reduction of Network Datasets for DDoS Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F25%3A10133837" target="_blank" >RIV/63839172:_____/25:10133837 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11297489" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11297489</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/CNSM67658.2025.11297489" target="_blank" >10.23919/CNSM67658.2025.11297489</a>
Alternative languages
Result language
angličtina
Original language name
Experiments with Reduction of Network Datasets for DDoS Analysis
Original language description
DDoS analysis and precise mitigation are still challenges due to more sophisticated DDoS attacks, their growing volume, and the diversity of network traffic itself. The machine learning methods enable automated analysis and subsequent mitigation by learning the legitimate traffic to be able to infer the boundary between the current DDoS and legitimate traffic during an attack. Since processing large packet samples is costly, especially if the sample is used during the DDoS analysis online, this paper assembles and evaluates several pipelines to reduce a large legitimate capture into a compact but representative packet sample for the timely analysis. The quality of the reduction is evaluated statistically and based on the resulting effectiveness of the ML method. The results show that the reduction pipelines produce samples with higher variability and contribute to the creation of boundaries that include a smaller proportion of legitimate traffic during mitigation than when using an unreduced sample of the same size.
Czech name
—
Czech description
—
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
2025
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 the 2025 21st International Conference on Network and Service Management (CNSM)
ISBN
978-3-903176-75-1
ISSN
2165-963X
e-ISSN
—
Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Bologna, Italy
Event location
Bologna, Italy
Event date
Oct 27, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—