Detection of DDoS Attacks on Clouds Computing Environments Using Machine Learning Techniques

The growing number of cloud-based services has led to a rising threat of Distributed Denial of Service (DDoS) attacks. These attacks can cause significant harm to businesses and organizations by overwhelming their network resources, resulting in the unavailability of critical services. The traditional defense mechanisms, such as firewalls systems, are becoming insufficient to cope with the scale and complexity of DDoS attacks. In this research, we propose a new machine learning approach based on ensemble learning to detect DDoS attacks in cloud environments. The proposed method utilizes various features extracted from network traffic to train machine learning algorithms. The proposed solution is expected to be effective in detecting DDoS attacks in real-time with high accuracy.