The Internet is used practically everywhere in today's digital environment. With the increased use of the Internet
comes an increase in the number of threats. DDoS attacks are one of the most popular types of cyber-attacks nowadays. With the
fast advancement of technology, the harm caused by DDoS attacks has grown increasingly severe. Because DDoS attacks may
readily modify the ports/protocols utilized or how they function, the basic features of these attacks must be examined. Machine
learning approaches have also been used extensively in intrusion detection research. Still, it is unclear what features are
applicable and which approach would be better suited for detection. With this in mind, the research presents a machine
learning-based DDoS attack detection approach. To train the attack detection model, we employ four Machine Learning
algorithms: Decision Tree classifier (ID3), k-Nearest Neighbors (k-NN), Logistic Regression, and Random Forest classifier.
