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.
| Mualliflar | Рахматов, Ф.А., Холмуминов, О.Т. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2025-09-20 |
| Jild | 8 |
| Son | 3 |
| Betlar | 94-100 |
| Til | Rus |
| DOI | 10.62132/ijdt.v8i3.292 |
DOI: 10.62132/ijdt.v8i3.292 · Maqolaning asl sahifasi
набор данных, машинное обучение, DDoS, CIC-DDoS2019, KNN, Random Forest, SVM, SYN Flood, UDP Flood, HTTP Flood, dataset, machine learning, DDoS, CIC-DDoS2019, KNN, Random Forest, SVM, SYN Flood, UDP Flood, HTTP Flood
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