A System Framework for Efficiently Recognizing Web Crawlers
Weiping Zhu, Jiangbo Qin, Ruoshan Kong, Hai Bo Lin, Zongjian He · 2018
In recent years, web crawlers are widely used for collecting data from the Internet. However, they cause many problems including QoS degrading of normal visits, inaccuracy of data analysis, and business concerns about the data in the websites. It is highly demanded that there is a systematical way to recognize the web crawlers. In this paper, we propose a system framework to recognize the web crawlers and take corresponding actions for handle them. The access requests of a website are recorded by the logs, and then a machine learning approach is used to distinguish the web crawlers from normal users based on the logs. Detail components and procedures of the system framework are illustrated. Based on the system framework and approach, we implement an anti-crawler system. A twenty-days experiment show that the system can recognize most of the requests from web crawlers and have few miss detections of accesses from humans as those from web crawlers.