Domain Name Server Filtering Service Using Threat Intelligence and Machine Learning Techniques

The Domain Name System (DNS) represents an indispensable pillar of the internet, facilitating the translation of user-friendly domain names into numerical IP addresses. In the rapidly evolving landscape of cybersecurity, the integration of threat intelligence feeds and advanced artificial intelligence techniques has become imperative for enhancing the efficacy of DNS filtering services. This paper proposes a novel approach to DNS filtering, leveraging real-time threat intelligence feeds and cutting-edge AI/ML algorithms. The research explores the synergies between threat intelligence and AI/ML to fortify DNS filtering capabilities, offering a proactive defense strategy against malicious activities such as phishing, malware, and command-and-control communications. Through the utilization of dynamic threat feeds, our system can adapt in real-time to emerging cyber threats, ensuring a timely response to evolving attack vectors. Furthermore, the paper discusses the design, implementation, and evaluation of the proposed DNS filtering service with efficient resolving capabilities.