Рақамли технологияларнинг назарий ва амалий масалалари Том 8 № 3 (2025) · с. 94-100
Analysis of the Performance Metrics of Existing Datasets for DDoS Attack Detection
Рахматов, Ф.А., Холмуминов, О.Т.
Аннотация
To effectively detect and prevent DDoS (Distributed Denial-of-Service) attacks, it is necessary to use various datasets. This article analyzes the most popular datasets for DDoS attack detection, including CIC-DDoS2019, NSL-KDD, UNSW-NB15, BoT-IoT, and CAIDA, evaluating their performance metrics. Each dataset is assessed based on attack types, size, real-time proximity, and usability. Furthermore, the accuracy and F1-score metrics of machine learning models on these datasets are compared. The results indicate that the CIC-DDoS2019 dataset is the most comprehensive and close to real-world scenarios, providing high performance with Random Forest and SVM algorithms. The study guides in selecting the optimal dataset and model combination for DDoS attack detection.
набор данныхмашинное обучениеDDoSCIC-DDoS2019KNNRandom ForestSVMSYN FloodUDP FloodHTTP Flood
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