A computer system in cyberspace is susceptible to various attacks, a major concern for organizations to detect and defend. Such a cyberattack is DDoS attack that makes challenge in the cybersecurity and higher alarm to organizations. Due to unique characteristics of DDoS attack, the modern developed security technologies still regarded the attack as elevated threat that aims to render unavailability of resources or services to its intended users. Our understanding of threats and how to counter them is being challenged by the rapidly evolving technologies. Hence, flaws in the networked system security may persist for the foreseeable future. Keeping this in view, we have designed a process framework that is capable of detecting attempts of intrusion in real time. We studied the Distributed Denial-of-Service (DDoS) attack and the implementation details through machine learning algorithms. The dataset used in this paper consists of network traffic captured during a simulated attack scenario and trained the machine learning models using packet headers and payload features. In this work, we conduct a comparative study of eight classifiers and suggest a model with improved predictive analysis and accuracy. Each machine learning algorithm’s performance is assessed using a number of metrics, such as F1-score, accuracy, precision, and recall. The studies’ findings demonstrated that the random forest model is the most accurate in identifying DDoS attacks.