Towards Proactive Domain Name Security: An Adaptive System for .ro domains Reputation Analysis

In a digital landscape marked by the exponential growth of cyber threats, the development of automated domain reputation systems is extremely important. Emerging technologies such as artificial intelligence and machine learning now enable proactive and scalable approaches to early identification of malicious or suspicious domains. This paper presents an adaptive domain name reputation system that integrates advanced machine learning to enhance cybersecurity resilience. The proposed framework uses domain data from .ro domain Registry and several other sources (blacklists, whitelists, DNS, SSL certificate), detects anomalies using machine learning techniques, and scores domain security risk levels. A supervised XGBoost model is trained and assessed through five-fold stratified cross-validation and a held-out 80/20 split. On an example dataset of 25,000 domains, the system attains accuracy 0.993 and F1 0.993 and is exposed through a lightweight Flask service that performs asynchronous feature collection for near real-time scoring. The contribution is a blueprint that links list supervision with registry/DNS/TLS features and deployable inference to support proactive domain abuse mitigation in ccTLD environments.