The increasing number of individuals connecting to the Internet has made Internet security a significant worry in contemporary society. Every minute, an immense quantity of information, amounting to trillions, is being collected. Although statistics series is advantageous, it could additionally be exploited via people with evil intentions. To mitigate those attacks, instructing users approximately the ability dangers and imparting them with the expertise to prevent such attacks is critical. Users can also set up a strong protection mechanism in opposition to potential attackers with the aid of imposing powerful security measures. This venture targets to increase a Machine Learning (ML) version to identify Distributed Denial-of-Service (DDoS) attacks. The version includes system studying algorithms along with Support Vector Classifier (SVC), K-Nearest Neighbours, Gaussian Naive Bayes (GaussianNB), and Random Forest Classifier. The SVC version executed an accuracy of 77.83%, the K-Nearest Neighbours version performed an accuracy of 78.16%, the Gaussian NB model done an accuracy of 81.5%, and the Random Forest classifier performed an accuracy of 80.66%.