Thesis Details

Optimization of DDoS Mitigation Rule Inference

Bachelor's Thesis Student: Carasec Elena Academic Year: 2021/2022 Supervisor: Žádník Martin, Ing., Ph.D.
Czech title
Optimalizace odvozování DDoS filtračních pravidel
Language
English
Abstract

This thesis discusses the possibility of using machine learning algorithms for DDoS protection. For classical and incremental (online) learning are considered explainable supervised learning methods, particularly decision trees. Furthermore, some possible optimisations are introduced to increase traffic classification accuracy and decrease the amount of blocked legitimate traffic.

Keywords

DDoS attack, filtration, machine learning, classification, supervised learning, incremental learning, data stream, decision tree, Explainable AI (XAI).

Department
Degree Programme
Files
Status
defended, grade B
Date
16 June 2022
Reviewer
Committee
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS FIT BUT), předseda
Beran Vítězslav, Ing., Ph.D. (DCGM FIT BUT), člen
Křena Bohuslav, Ing., Ph.D. (DITS FIT BUT), člen
Orság Filip, Ing., Ph.D. (DITS FIT BUT), člen
Citation
CARASEC, Elena. Optimization of DDoS Mitigation Rule Inference. Brno, 2022. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-16. Supervised by Žádník Martin. Available from: https://www.fit.vut.cz/study/thesis/24640/
BibTeX
@bachelorsthesis{FITBT24640,
    author = "Elena Carasec",
    type = "Bachelor's thesis",
    title = "Optimization of DDoS Mitigation Rule Inference",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "english",
    url = "https://www.fit.vut.cz/study/thesis/24640/"
}
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