Optimized CatBoost machine learning (OCML) for DDoS detection in cloud virtual machines with time-series and adversarial robustness.

Distributed Denial of Service (DDoS) attacks represent one of the most strategically executed and severe threats in cloud computing, often leading to substantial data loss and significant financial damage for both cloud service providers and their users. Numerous studies have been conducted to enhance cloud security against such attacks through the application of machine learning techniques. This paper implements the Optimized Catboost machine learning algorithm (OCML) with hyperparameter optimization using Optuna to achieve efficient training. Feature selection was conducted using the SHAP (SHapley Additive exPlanations) method, as the dataset contains over 80 features. The proposed model achieved an accuracy of 99.2% in detecting Distributed Denial of Service (DDoS) attacks in cloud virtual machines (VMs), enabling the system to filter out malicious jobs and allocate resources efficiently. The CICIDS 2019 dataset was used as the benchmark for evaluation. Furthermore, the robustness of the proposed model was assessed using adversarial attacks, specifically the Fast Gradient Sign Method (FGSM), the Carlini-Wagner (CW) attack, and Projected Gradient Descent (PGD). The Catboost model achieves accuracies against these attacks 97%, 80% and 71% respectively. In addition, the robustness against time series network traffic attacks using pulse wave, random burst, and slow ramp achieves 80%, 83% and 77% respectively.