Denial of Service Attack

Denial-of-service (DoS) attacks aim to disrupt online services by overwhelming target systems with traffic or requests. Current research focuses on mitigating DoS attacks across diverse systems, from large language models (LLMs) vulnerable to cleverly crafted prompts, to IoT devices susceptible to flooding attacks, and even congestion control algorithms prone to adversarial traffic patterns. Researchers are employing machine learning techniques, including deep learning architectures like convolutional neural networks (CNNs) and transformers (with attention mechanisms), to detect and defend against these attacks, with a particular emphasis on improving detection accuracy and speed. The effectiveness of these methods in securing increasingly interconnected systems is crucial for maintaining the reliability and availability of online services.