Enterprise AI Analysis

The OCML Framework: A Multi-faceted Approach

The OCML framework combines three critical mechanisms for superior DDoS detection:

• Optuna-based Bayesian Hyperparameter Optimization: Ensures efficient training and optimal performance by precisely tuning model parameters.
• SHAP-driven Feature Selection: Reduces dimensionality by identifying the 10 most influential traffic indicators, enhancing model interpretability and reducing computational overhead by 87.5%.
• Adversarial Training with PGD: Strengthens model resilience against gradient-based perturbations, specifically against attacks like FGSM, CW, and PGD.

The model leverages CatBoost for its robust handling of categorical features and noisy data, trained and evaluated on the comprehensive CICIDS 2019 dataset to simulate realistic cloud VM environments.

Quantified Performance and Efficiency Gains

The OCML model achieved a baseline accuracy of 99.2% in detecting DDoS attacks, marking a 1.5% improvement over the best prior CatBoost models. This high accuracy is complemented by a remarkably low false-positive rate of 0.5%, critical for minimizing operational disruptions. The framework's ability to operate efficiently with only 10 key features (an 87.5% reduction in feature space) significantly reduces computational load while maintaining peak performance, making it highly scalable for enterprise cloud deployments.

Against white-box adversarial attacks, OCML demonstrated accuracies of 97% (FGSM), 80% (CW), and 71% (PGD). For dynamic time-series attacks (pulse wave, random burst, slow ramp), F1-scores ranged from 0.77 to 0.83, confirming its adaptability to evolving threat behaviors.

Building Unwavering Resilience Against Advanced Threats

A core strength of the OCML framework is its demonstrated robustness against sophisticated cyber threats. Through rigorous adversarial training, the model achieved a mean adversarial accuracy of 84.4% across FGSM, CW, and PGD attacks, reducing the attack success rate by 17.3% compared to an untuned baseline. This robust defense ensures that the system remains highly effective even when subjected to expertly crafted evasion attempts.

Furthermore, the model's performance was validated against time-series network traffic, including pulse wave, random burst, and slow ramp attacks, yielding F1-scores of 80%, 83%, and 77% respectively. This confirms OCML's capability to detect evolving attack patterns, a crucial factor for real-world cloud security where attack dynamics are constantly changing.

Strategic Advantages and Future Directions

The OCML framework represents a significant leap in cloud security, offering enterprises a highly accurate and resilient solution for DDoS detection. Its integration of Optuna, SHAP, and adversarial training provides a robust defense against static and dynamic threats, ensuring data confidentiality and operational stability in virtualized environments. This proactive defense mechanism enables efficient resource allocation and minimizes financial and reputational damage.

Future research will focus on real-world deployments in environments like AWS, Azure, or GCP to validate scalability and latency, explore adaptive learning strategies, and integrate with real-time monitoring systems for continuous threat detection and mitigation.