The rapid adoption of cloud computing has revolutionized how businesses and consumers access and utilize resources, offering scalability, flexibility, and cost effectiveness. However, this increased reliance on cloud services has also led to a rise in Distributed Denial of Service (DDoS) attacks, which can severely impact the availability and performance of these services. This study aims to address the critical need for effective detection and classification of DDoS attacks in cloud environments using machine learning techniques. We conducted binary and multiclass classification experiments using the CICDDoS2019 dataset, focusing on three specific types of attacks. Four machine learning models, namely Random Forest, K Nearest Neighbor, Naïve Bayes, and Logistic Regression, were implemented in a Kaggle notebook using Python. Feature selection techniques, including Chi square and Principal Component Analysis, were employed to identify the most relevant features, while the oversampling technique was used to handle imbalanced data. The experiments yielded impressive results, with Random Forest and K-Nearest Neighbor achieving the highest accuracy rates of 100% and 99.72% in binary classification, and 100% and 99.66% in multiclass classification, respectively. The study also measured training and testing times, along with other performance metrics. These findings highlight the effectiveness of machine learning approaches in tackling cloud based detection challenges while ensuring computational efficiency tailored for dynamic cloud environments.

Machine Learning, DDoS Attacks, DoS attacks, Cloud Security