Unveiling Domain Generation Algorithms in DNS Log Traffic: A Next-Generation Intelligent Framework for Dynamic Anomaly Detection and Mitigation through Machine Learning Analysis

Pioneering the forefront of network security innovation, this study embarks on an exhilarating exploration of DNS log data sourced from the esteemed Anna University, propelled by the cutting-edge prowess of the “Adaptive Ensemble Anomaly Detection” (AEAD) technique. DNS logs serve as a treasure trove of network activities, encapsulating intricate domain resolution requests and responses. Through the transformative lens of AEAD, a pioneering and dynamic machine learning paradigm, this research endeavors to illuminate hidden patterns and anomalies lurking within the intricate fabric of DNS log data. AEAD’s adaptive prowess situates it at the pinnacle of anomaly detection and mitigation methodologies, perpetually evolving to confront the ever-shifting challenges of contemporary network landscapes. By meticulously dissecting DNS logs with surgical precision, this study lays the cornerstone for the development of resilient strategies in network management and security. Remarkably, this approach boasts an unparalleled accuracy rate of 99.98%, cementing its status as an indispensable tool for safeguarding digital ecosystems.