Národní úložiště šedé literatury Nalezeno 1 záznamů.  Hledání trvalo 0.01 vteřin. 
Optimization of DDoS Attack Mitigation based on Machine Learning
Banák, Filip ; Šišmiš, Lukáš (oponent) ; Kučera, Jan (vedoucí práce)
DDoS attacks using the TCP protocol are still amongst the most common. This thesis aims to take advantage of information present in TCP SYN messages to improve DDoS attack detection success rate. TCP SYN fingerprints are proposed as an additional data source to consider when computing features for DDoS detection. A combination of an existing feature extraction and aggregation system with an existing autoencoder-based anomaly detector is significantly optimized and extended to make use of SYN fingerprints. The experimental results show decent DDoS detection improvements on relevant datasets. The detector is 16 and 95 times faster to train and execute respectively. The extraction and aggregation system is 23 times faster.

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