Robust DDoS attack detection with adaptive transfer learning

In the evolving cybersecurity landscape, the rising frequency of Distributed Denial of Service (DDoS) attacks requires robust defense mechanisms to safeguard network infrastructure availability and integrity. Deep Learning (DL) models have emerged as a promising approach for DDoS attack detection and mitigation due to their capability of automatically learning feature representations and distinguishing complex patterns within network traffic data. However, the effectiveness of DL models in protecting against evolving attacks depends also on the design of adaptive architectures, through the combination of appropriate models, quality data, and thorough hyperparameter optimizations, which are scarcely performed in the literature. Also, within adaptive architectures for DDoS detection, no method has yet addressed how to transfer knowledge between different datasets to improve classification accuracy. In this paper, we propose an innovative approach for DDoS detection by leveraging Convolutional Neural Networks (CNN), adaptive architectures, and transfer learning techniques. Experimental results on publicly available datasets show that the proposed adaptive transfer learning method effectively identifies benign and malicious activities and specific attack categories.