This paper proposes an intelligent system, called IDGADS, for detecting algorithmically generated domains in the early stages based on easy and automatic computable features of real domain name system (DNS) traffic quickly without investing time in reverse engineering and/or log monitoring or dependency on external information like WHOIS/DNS response. IDGADS is a supervised deep learning model, trained over 17M domains from the reputed sources. It is implemented in Python and served as a service over the cloud for free testing of the public in the form of a web app. IDGADS is capable of detecting malicious domains up to 99% accuracy. Till 17-April-2020, 1160963 domains have been tested, and it has detected 817069 DGA-generated domains by the users of different countries. Since IDGADS is developed to check DNS queries only, thus it can be installed as the first line of defence in security stack for validating DNS queries before sending to DNS server.

本文提出了一种名为IDGADS的智能系统，该系统可根据实域名系统（DNS）流量的便捷和自动计算功能，在早期阶段检测算法生成的域，而无需花费大量时间进行逆向工程和/或日志监控或依赖外部信息，例如WHOIS / DNS响应。IDGADS是一种受监督的深度学习模型，在来自知名来源的1700万个域上进行了训练。它是用Python实现的，可作为云上的服务，以Web应用程序的形式对公众进行免费测试。IDGADS能够检测高达99％的准确性的恶意域。到2020年4月17日，已经测试了1160963个域，并且它已检测到817069 DGA生成的域被不同国家的用户使用。由于IDGADS被开发为仅检查DNS查询，