Research on Security Solutions for Denial-of-Service (DoS) Attacks in Cloud Computing focuses on detecting, preventing, and mitigating attacks that disrupt service availability. Modern approaches employ deep learning, machine learning, and statistical techniques to identify anomalous traffic patterns and distinguish legitimate requests from malicious ones. Defense strategies include packet filtering, rate limiting, intrusion detection systems, Cloud Protection Platforms, and elastic load balancing to absorb attack traffic. Advanced frameworks such as PCA-based early detection, supervised learning models, and hybrid AI-driven systems enhance real-time monitoring and automated response capabilities. Overall, these solutions aim to maintain high availability, minimize service disruption, and strengthen the resilience of cloud infrastructures against evolving DoS and DDoS attack vectors.