The widespread adoption of Software-Defined Networking (SDN) in medium-scale information and communication systems increases network management flexibility but introduces security risks due to the centralized controller. Various approaches based on Machine Learning, Deep Learning, statistical analysis, Reinforcement Learning, and Federated Learning have been proposed to predict cyberattacks in SDN networks. However, most existing solutions do not fully meet real-time, resource-efficient, and adaptive requirements. This paper provides an analytical review of related work and proposes a hybrid adaptive KBGM framework integrating statistical analysis, Random Forest, LSTM, RL, and FL methods to achieve high accuracy and efficient real-time performance.
| Mualliflar | Jumayev, Sodiq |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-12-23 |
| Son | 4 |
| Betlar | 225-230 |
| Til | Rus |
Software-Defined Networking (SDN); прогнозирование кибератак; обнаружение аномалий; Random Forest; LSTM; обучение с подкреплением (Reinforcement Learning); федеративное обучение (Federated Learning); средние по размеру информационно-коммуникационные системы., Software-Defined Networking (SDN); cyberattack prediction; anomaly detection; Random Forest; LSTM; Reinforcement Learning; Federated Learning; medium-scale information and communication systems., Software-Defined Networking (SDN), kiberhujumlarni bashorat qilish, anomalani aniqlash, Random Forest, LSTM, Reinforcement Learning, Federated Learning, o‘rta hajmli AKT tizimlari.
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