This paper proposes an adaptive transfer learning approach for robust DDoS attack detection using deep learning (DL) models. The method leverages Convolutional Neural Networks (CNNs), adaptive architectures, and transfer learning techniques to enhance the accuracy and efficiency of detecting benign and malicious network traffic. The approach involves data preprocessing, CNN model design, transfer learning, hyperparameter optimization, and model evaluation. The proposed framework is tested on publicly available datasets, including KDDCup'99, UNSW-NB15, CSE-CIC-IDS2018, and CIC-DDoS2019. The results show that the adaptive transfer learning method achieves high accuracy in distinguishing benign from malicious traffic and identifying specific attack types. The study also evaluates the performance of pre-trained models such as VGG16, VGG19, and ResNet50, demonstrating that VGG19 performs exceptionally well in both binary and multi-class classification tasks. The results indicate that the proposed adaptive transfer learning approach significantly improves DDoS attack detection accuracy compared to traditional DL models and other state-of-the-art methods. The study highlights the effectiveness of transfer learning in enhancing model adaptability and performance, particularly in scenarios with limited labeled data. The findings suggest that adaptive transfer learning is a promising approach for robust DDoS attack detection in dynamic network environments.This paper proposes an adaptive transfer learning approach for robust DDoS attack detection using deep learning (DL) models. The method leverages Convolutional Neural Networks (CNNs), adaptive architectures, and transfer learning techniques to enhance the accuracy and efficiency of detecting benign and malicious network traffic. The approach involves data preprocessing, CNN model design, transfer learning, hyperparameter optimization, and model evaluation. The proposed framework is tested on publicly available datasets, including KDDCup'99, UNSW-NB15, CSE-CIC-IDS2018, and CIC-DDoS2019. The results show that the adaptive transfer learning method achieves high accuracy in distinguishing benign from malicious traffic and identifying specific attack types. The study also evaluates the performance of pre-trained models such as VGG16, VGG19, and ResNet50, demonstrating that VGG19 performs exceptionally well in both binary and multi-class classification tasks. The results indicate that the proposed adaptive transfer learning approach significantly improves DDoS attack detection accuracy compared to traditional DL models and other state-of-the-art methods. The study highlights the effectiveness of transfer learning in enhancing model adaptability and performance, particularly in scenarios with limited labeled data. The findings suggest that adaptive transfer learning is a promising approach for robust DDoS attack detection in dynamic network environments.