Modern electronic resources and web applications are exposed to numerous cyber threats, including DDoS attacks, SQL injections, XSS attacks, Slowloris, and multi-vector attacks.This article presents an intelligent system for detecting and preventing cyber threats, developed based on hybrid traffic analysis methods.The system architecture utilizes SDN/NFV technologies for dynamic scaling and integrates machine learning algorithms: PNHAD (Poisson-Normal Hybrid Anomaly Detection), XGBoost, CRNN/LSTM, and the hybrid adaptive ensemble HAEDFS.Experimental studies on the open datasets CIC-IDS2017 and UNSW-NB15 demonstrated high attack classification accuracy (up to 99.95%) with a low false positive rate (<0.5%).The proposed solution provides adaptive and intelligent protection for electronic resources and can be recommended for implementation in critical infrastructures.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2025-09-12 |
| DOI | 10.61663/252tuitmct4 |
DOI: 10.61663/252tuitmct4 · Maqolaning asl sahifasi
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