DefendNet: Harnessing AI/ML for Dynamic DNS Filtering and Network Security

The ubiquitous nature of the Internet has changed the nature of interaction, business, and work, but it has also provided an easy way for people with bad intentions to take advantage of the vulnerable system and use it for evil activities. Compromising the DNS backbone is an example of malwares and botnets’ capabilities of exploiting the infrastructure of the Internet. DNS-based attacks are more evolved now than ever before. In addition to this, traditional defense mechanisms like rule-based approaches and threat intelligence feeds are immensely limited in scope and ability to deal with evolving cyber threats which makes individuals and organizations susceptible to newer cyber-attacks. To provide an efficient solution for these problems, this paper proposes a machine learning and ensemble model technique for enhanced DNS security. The proposed system implements the ensemble learning approaches, particularly Gradient Boosting Machines (GBM), XGBoost, and CatBoost, along with sophisticated preprocessing techniques and optimization methods to maximize the precision and performance of the model. Real-time threat detection and alert systems teamed with a user-friendly dashboard, empower users by giving them all the necessary know-how for safeguarding their digital assets effectively. Given architecture integrates components such as API or Proxy, Local Cache and Whitelisting, Blacklisting, Website Scanner and Antivirus Engines, and Machine Learning Model to create a robust defense mechanism against such cyber threats. Through a meticulously designed workflow, the system intercepts DNS requests, conducts real-time analysis and filtering, and employs machine learning models to detect malicious domains, ensuring secure browsing experiences for users while mitigating risks associated with malicious URLs. The solution emphasizes improving the throughput and decreasing the latency of DNS requests and response times. By optimizing the processing pipeline & employing efficient algorithms, the system aims to expedite the resolution process and minimize the time it takes to serve DNS requests. Overall, our system employs machine learning and ensemble techniques to push DNS security to the next level, but at the same time gives priority to data throughput and reduces the latency to improve the performance of the system, making the digital assets secure for cyber security and satisfactory user experience.