This study presents an innovative method that uses PCA for DDoS attack detection and mitigation techniques include feature selection and a hybrid neural network (DeepDDos) model. This model includes Conv1D layers for extracting features, MaxPooling layer for dimensionality reduction, and a GRU layer for capturing sequential patterns. Dropout layers mitigate overfitting, while Flatten layers prepare data for analysis. Conv1D layers enhance the model’s ability to identify DDoS attack patterns. MaxPooling layers reduce spatial dimensions while preserving important information. The GRU layer captures temporal dependencies, facilitating robust attack pattern identification. The model incorporates MLP layers for classification, including three Dense layers. Empirical assessment confirms the model’s effectiveness in precisely identifying and mitigating DDoS attacks, thereby strengthening cybersecurity defenses against advancing threats.