Behavioral Analysis of Domain Name System (DNS) Attacks and the Development of Innovative Countermeasures Using the Random Forest Algorithm: AI-Based Systematic Integration

Abstract

The Domain Name System (DNS) is a fundamental component of the Internet's infrastructure and has become a frequent target of major cyberattacks, such as DNS Spoofing, DNS Tunneling, and DNS Amplification Attacks. Among these, DNS Amplification Attacks are the most dangerous, as they exploit misconfigured DNS servers to amplify traffic and overwhelm the target with massive amounts of data. These attacks are particularly challenging for conventional detection techniques to analyze and mitigate. This research proposes an enhanced real-time DNS threat detection model based on the Random Forest algorithm. By utilizing attributes such as query type, packet size, and response time, the model achieves a 98% accuracy rate in distinguishing between normal and anomalous traffic. Additionally, false positives are reduced to 5%, and the response time is improved by 120 milliseconds compared to previously implemented solutions. The success of these network classification models consistently demonstrates the effectiveness of ensemble methods, particularly in addressing DNS threats. Future work will focus on advancing detection systems by developing hybrid models and incorporating signal processing techniques that leverage real-time analysis. This approach aims to ensure that newly emerging cyber threats are effectively identified and mitigated.