Distributed Denial of Service

Distributed Denial-of-Service (DDoS) attacks overwhelm online systems with excessive traffic, disrupting services and compromising data; research focuses on developing robust detection and mitigation strategies. Current efforts leverage various machine learning and deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and transformer-based architectures, often enhanced with attention mechanisms and ensemble methods to improve accuracy and efficiency. These advancements are crucial for securing increasingly interconnected systems, particularly in the context of IoT and 5G networks, and contribute to a more resilient and secure digital infrastructure.