Internet usage has become essential for effective and timely communication, e-commerce activities, and financial transactions, contributing to a more sophisticated lifestyle. However, these activities are increasingly vulnerable to internet threats and fraud. A Distributed Denial of Service (DDoS) attack is a prevalent internet threat that disrupts the normal traffic of a victim server by overwhelming the target infrastructure with a flood of internet traffic. The primary aim of attackers is to create uncertainty for individuals or organizations, typically seeking financial gain or aiming to damage an organization's reputation. Notably, during the Russia-Ukraine war, significant DDoS attacks targeted Ukrainian bank servers to disrupt financial services for customers. This study employs the Naive Bayes model with 10-fold cross-validation to detect DDoS attacks. Naive Bayes, a widely recognized machine learning algorithm, demonstrated superior performance. The results revealed an average accuracy of 0.999, outperforming existing machine learning-based DDoS attack detection techniques.