Network Flow based Abnormal Behavior feature Extraction for DDoS Attack Classification used Adaptive Federated Learning Model

The use of Internet apps has increased dramatically everywhere. The popular When a web server is subjected to a Distributed Denial of Service (DDoS) assault, its resources are unable to function normally. The DDoS assault results in network server congestion, which delays web services and results in large financial losses; as a result, proactive and prompt action is needed. The study offers a thorough analysis of the crucial performance criteria for assessing the performance of various defence solutions in a network context. It is feasible to automatically differentiate between high-level and low-level features thanks to deep learning algorithms resulting in efficient representation and inference. To identify patterns in streams of network traffic and keep track of network attack operations, we construct a recurrent deep neural network. The results of the experiments show that our method outperforms more well-known machine learning techniques. To build a deep learning model that can forecast DDoS strikes, this work uses multiple regression analysis to take into consideration the most popular benchmark dataset and investigate the difficulty of identifying DDoS attacks in a cloud context.