In today’s digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification. Display omitted •An innovative hybrid feature extraction method for training of DDoS attack detection model.•We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks.•Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models.•The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score).•The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature.