A real-time machine-learning model for detecting and mitigating DDoS attacks

Abstract Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are among the most lethal cyber threats in this world, which make an online service unavailable to its legitimate users by overwhelming the service provider’s resources. Regarding the importance of online services in a human's life, researchers have been working on techniques to detect and mitigate these kinds of attacks. Machine-learning models showed acceptable performance in DDoS detection. Hence, in this paper, we developed a machine-learning model for classifying network traffic and detecting DDoS attacks using a unique approach to pre-process the data. The most innovative aspect of our work is deploying our developed machine-learning model into an online real-time DDoS detection system and testing it under real DDoS attacks. Implementing and testing a DDoS detection system that can work outside of a dataset and can be used against real threats was the missing part of other similar works that were done in this paper. The model on offline data and the system under real attacks both showed great performance in detecting attack traffic with accuracies of 99.99% and 95.30%, respectively, and proved they can effectively be used against DDoS attacks.