This paper presents an innovative approach for robust detection of Distributed Denial of Service (DDoS) attacks using adaptive transfer learning. The authors propose a method that leverages Convolutional Neural Networks (CNNs), adaptive architectures, and transfer learning techniques to enhance the accuracy of DDoS attack detection. The proposed method includes data preprocessing, CNN model selection, transfer learning, hyperparameter optimization, and model evaluation. Experimental results on four well-known datasets (KDDCup'99, UNSW-NB15, CSE-CIC-IDS2018, and CIC-DDoS2019) demonstrate the effectiveness of the proposed approach in identifying benign and malicious network traffic, as well as specific attack categories. The study shows that the adaptive transfer learning method achieves high accuracy in both binary and multi-label classification tasks, outperforming state-of-the-art methods in DDoS attack detection. The paper also discusses the importance of hyperparameter optimization and the adaptability of the models to different datasets, highlighting the potential of the proposed approach in enhancing the robustness and efficiency of DDoS attack detection systems.This paper presents an innovative approach for robust detection of Distributed Denial of Service (DDoS) attacks using adaptive transfer learning. The authors propose a method that leverages Convolutional Neural Networks (CNNs), adaptive architectures, and transfer learning techniques to enhance the accuracy of DDoS attack detection. The proposed method includes data preprocessing, CNN model selection, transfer learning, hyperparameter optimization, and model evaluation. Experimental results on four well-known datasets (KDDCup'99, UNSW-NB15, CSE-CIC-IDS2018, and CIC-DDoS2019) demonstrate the effectiveness of the proposed approach in identifying benign and malicious network traffic, as well as specific attack categories. The study shows that the adaptive transfer learning method achieves high accuracy in both binary and multi-label classification tasks, outperforming state-of-the-art methods in DDoS attack detection. The paper also discusses the importance of hyperparameter optimization and the adaptability of the models to different datasets, highlighting the potential of the proposed approach in enhancing the robustness and efficiency of DDoS attack detection systems.