Thesis Details
Behaviorální analýza síťových útoků typu DDoS
This bachelor thesis deals with anomaly detection in computer networks using artificial intelligence method. Main focus is on the detection of DDoS attacks based on the information from the lower layers of the OSI model. The target is to design and implement a system that is capable of detecting different types of DDoS attacks and characterize common features among them. Selected attacks are SYN flood, UDP flood and ICMP flood. Description and feature selection of the attacks is included. Furthermore, a system is designed that evaluates whether the network traffic (organized into flows) is a DDoS attack or not. Attacks are detected using the XGBoost method, which uses supervised learning. The final model is validated using cross-validation and tested on attacks generated by the author.
DoS, DDoS, anomaly detection, XGBoost, SHAP values
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Kořenek Jan, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Křena Bohuslav, Ing., Ph.D. (DITS FIT BUT), člen
Orság Filip, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT25124,
author = "Ondrej Kvasnica",
type = "Bachelor's thesis",
title = "Behavior\'{a}ln\'{i} anal\'{y}za s\'{i}\v{t}ov\'{y}ch \'{u}tok\r{u} typu DDoS",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/25124/"
}