Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Mitigation of DoS Attacks Using Machine Learning
Goldschmidt, Patrik ; Kekely, Lukáš (oponent) ; Kučera, Jan (vedoucí práce)
Distributed Denial of Service (DDoS) attacks are an ever-increasing type of security incident on modern computer networks. This thesis aims to detect these attacks and provide relevant information in order to mitigate them in real-time. This functionality is achieved by data stream mining and machine learning techniques. The output of the work is a series of tools executing the process of the whole machine learning pipeline - from custom feature extraction through data preprocessing to exporting a trained model ready for deployment. The experimental results evaluated on various real and synthetic datasets indicate an accuracy of over 99% with an ability to reliably detect an ongoing attack within the first 4 seconds of its start.
Mitigation of DoS Attacks Using Machine Learning
Goldschmidt, Patrik ; Kekely, Lukáš (oponent) ; Kučera, Jan (vedoucí práce)
Distributed Denial of Service (DDoS) attacks are an ever-increasing type of security incident on modern computer networks. This thesis aims to detect these attacks and provide relevant information in order to mitigate them in real-time. This functionality is achieved by data stream mining and machine learning techniques. The output of the work is a series of tools executing the process of the whole machine learning pipeline - from custom feature extraction through data preprocessing to exporting a trained model ready for deployment. The experimental results evaluated on various real and synthetic datasets indicate an accuracy of over 99% with an ability to reliably detect an ongoing attack within the first 4 seconds of its start.

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