Multi-tiered DNS Architecture for Malicious Traffic Mitigation

The patent implements dynamic detection by transitioning from static fixed signatures to adaptive machine learning models that continuously learn and update attack patterns. The system dynamically adjusts detection parameters and retrain models based on evolving traffic characteristics, enabling the detection mechanism to adapt to new attack patterns while maintaining operational simplicity through automated updates.

The patent extracts and focuses on the most critical features from the volumetric dataset, removing irrelevant and redundant measurements that cause over-fitting. The machine learning models identify and retain only the essential traffic characteristics needed for accurate anomaly detection, thereby maintaining high detection capability while eliminating the source of false alarms through feature selection and dimensionality reduction.

The patent applies local quality by assigning different levels of analysis to different traffic characteristics. Rather than uniformly processing all features with high complexity, the system identifies specific local patterns and anomalies in traffic behavior that are most indicative of malicious activity, focusing computational resources on these critical local characteristics while simplifying processing of less important features.

The patent introduces machine learning models as intermediary components between traffic monitoring and mitigation actions. These intermediary models analyze traffic patterns using multiple parameters and provide refined classification decisions, enabling the system to maintain operational simplicity at the decision-making layer while incorporating sophisticated multi-parameter analysis in the intermediate processing stage, thereby blocking malicious traffic more effectively without increasing overall system complexity.