DDoS (Distributed Denial of Service) attacks pose a serious challenge to online security by overwhelming servers with excessive traffic, making them inaccessible to legitimate users. The goal of such an attack is to overload the server's capacity, causing disruptions and service outages. These attacks are dangerous because they can be carried out with little effort and do not require advanced tools. A large number of infected devices, often referred to as bots, are remotely controlled by a single operator (botmaster), using fake IP addresses to avoid detection. This study focuses on evaluating different machine learning (ML) and deep learning (DL) methods for the detection and analysis of DDoS attacks. It will also highlight the differences between these two approaches, helping to determine the best scenarios for their use in addressing such threats.