As cyber threats continue to evolve, defending hybrid cloud environments from multi-vector DDoS attacks has become a critical challenge. Traditional detection systems, which often rely on static rules, struggle to adapt to dynamic traffic patterns and increasingly sophisticated attack strategies. This research proposes a behavior-based DDoS detection system that leverages advanced machine learning techniques, such as DBSCAN and Isolation Forest, to significantly enhance detection accuracy, reduce false positives, and improve response times. By dynamically adapting to shifting traffic behaviors in real-time, the system strengthens defenses, increases scalability, and ensures minimal service disruption, thereby protecting valuable revenue streams.

Hariprasad Sivaraman’s research, "Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments", introduces a pioneering behavior-based DDoS detection model for addressing multi-vector attacks in hybrid cloud environments. By utilizing machine learning techniques like DBSCAN and Isolation Forest, his approach tackles challenges such as dynamic traffic patterns and identity sprawl in hybrid clouds. Through adaptive thresholds and anomaly scoring, Sivaraman’s model reduces false positives and enhances detection precision, providing scalable and real-time solutions.