Rising cyber threats, with miscreants registering thousands of new domains
daily for Internet-scale attacks like spam, phishing, and drive-by downloads,
emphasize the need for innovative detection methods. This paper introduces a
cutting-edge approach for identifying suspicious domains at the onset of the
registration process. The accompanying data pipeline generates crucial features
by comparing new domains to registered domains,emphasizing the crucial
similarity score. Leveraging a novel combination of Natural Language Processing
(NLP) techniques, including a pretrained Canine model, and Multilayer
Perceptron (MLP) models, our system analyzes semantic and numerical attributes,
providing a robust solution for early threat detection. This integrated
approach significantly reduces the window of vulnerability, fortifying defenses
against potential threats. The findings demonstrate the effectiveness of the
integrated approach and contribute to the ongoing efforts in developing
proactive strategies to mitigate the risks associated with illicit online
activities through the early identification of suspicious domain registrations.

通过比较新注册的域名与已注册的域名的相似性分数，本研究引入了一种先进的方法来在注册过程开始时识别可疑域名。结合自然语言处理和多层感知器模型，该系统分析语义和数值属性，提供了早期威胁检测的强大解决方案，从而显著减少了漏洞窗口，加强了对潜在威胁的防御。研究结果证明了综合方法的有效性，并为通过早期识别可疑域名注册来减轻非法在线活动风险的持续努力做出了贡献。