A Distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt the normal functioning of a targeted server, service, or network by overwhelming it with a flood of internet traffic. This thesis presents a modern intelligent DDoS attack detection model based on federated learning and neural networks. This model is shortly called the DDoS-FLNN model. The proposed DDoS-FLNN uses a modern dataset called “CIC-Bell-DNS-EXF-Dataset” that is characterized by the diversity of attack behavior that allowed the derivation of five sub-datasets to suit the application of the Federated Learning approaches. The DDoS-FLNN proposed a hybrid Long Short-Term Memory + convolution neural network (LSTM + CNN) classification DDoS attack model by combining both models, with LSTM capturing temporal patterns from sequential data and CNN capturing spatial patterns for input data. The proposed hybrid LSTM + CNN aims to achieve optimal accuracy in DDoS attack detection.