A Machine Learning-Based Framework for Real-Time DNS Threat Detection and Mitigation Using Ensemble Models and Advanced Security Mechanisms

The Domain Name System (DNS) is the subject of more sophisticated assaults that have exposed weaknesses in the Internet's backbone, calling for more sophisticated remedies than just conventional rule-based and threat intelligence systems. This implementation paper targets the development of strong and robust cybersecurity systems against using machine learning and techniques developed under ensemble learning methodologies so as to strengthen DNS layer safety. The proposed system integrates GBM, XGBoost, CatBoost, and advanced data preprocessing, feature engineering, and optimization strategies to effectively identify and mitigate malicious DNS activities. Key components of the system include a real-time threat detection mechanism, user-friendly dashboard, and API/Proxy integration, local caching, blacklisting, and whitelisting features. The machine learning models are so specifically trained and launched to identify malicious URLs and domains that are generated by Domain Generation Algorithms (DGAs), thus providing proactive security against evolving cyber threats. Throughput is optimized, and latency minimized, within the DNS resolution pipeline so that browsing can be fast and secure. The design incorporates an unobstructive flow of intercepting DNS requests, real-time analysis of traffic, and automatic classification of domains with ensemble models. Results show high detection accuracy and lower response times than traditional approaches. The real-time alerts, detailed insights, and more power it gives to the user make this tool useful in securing their digital assets with actionable intelligence. This paper points out practical challenges of implementation, the solutions used, and the benchmarks in the performance of the system. It provides an efficient and scalable cybersecurity solution to organizations and individuals. The system has set new standards for DNS security. The integration of innovation with practicality results in a highly performing, secure user experience..