Using Dynamic Analysis to Automatically Detect Anti-Adblocker on the Web
Jingxue Sun, Ting Yang, Zhiqiu Huang, Wengjie Wang, Yuqing Zhang · 2021
With the continuous development of Internet technology, there are more and more advertisements on the website and some of them can track and monitor users. To avoid the leakage of privacy information, many people are using adblockers to remove advertisements on web pages. This behavior of filtering advertisements seriously threatens the benefits of online publishers, and they have begun to detect and counterattack users who use adblockers. Previous work focused on detecting and filtering anti-adblockers to fight against the counterattacks of online publishers. So far, one of the most effective ways to prevent anti-adblockers is to create a blacklist. However, the generation and maintenance of blacklists involve considerable manual work, which is inefficient and difficult to maintain. This paper proposes a machine learning anti-adblocker detection system called ABDetector, which is the first system that can automatically generate blacklists of anti-adblockers. This system greatly reduces the manual workload. Since the difference of code with and without anti-adblocking detection behavior mainly lies in that they call different APIs, thus, we for the first time tried to build a classifier of anti-adblockers using JavaScript APIs as features and use dynamic analysis method to extract features. Unlike static analysis method, the dynamic analysis method can effectively avoid code obfuscation. The accuracy of ABDetector on the test set is 81.46%.