The rise of ad-blockers is viewed as an economic threat by online publishers, especially those who primarily rely on advertising to support their services. To address this threat, publishers have started retaliating by employing ad-block detectors, which scout for ad-blocker users and react to them by restricting their content access and pushing them to whitelist the website or disabling ad-blockers altogether. The clash between ad-blockers and ad-block detectors has resulted in a new arms race on the web.

In this thesis, we present the first systematic measurement and analysis of ad-block detection on the web. We have designed and implemented a machine learning based technique to automatically detect ad-block detection, and use it to study the deployment of ad-block detectors o...[ Read more ]

The rise of ad-blockers is viewed as an economic threat by online publishers, especially those who primarily rely on advertising to support their services. To address this threat, publishers have started retaliating by employing ad-block detectors, which scout for ad-blocker users and react to them by restricting their content access and pushing them to whitelist the website or disabling ad-blockers altogether. The clash between ad-blockers and ad-block detectors has resulted in a new arms race on the web.

In this thesis, we present the first systematic measurement and analysis of ad-block detection on the web. We have designed and implemented a machine learning based technique to automatically detect ad-block detection, and use it to study the deployment of ad-block detectors on Alexa top-100K websites. The approach is promising with precision of 94.8% and recall of 93.1%. We characterize the spectrum of different strategies used by websites for ad-block detection. We find that a vast majority of publishers on the web use fairly simple passive approaches for ad-block detection. However, we also note that a few websites use third-party services, e.g. PageFair, for ad-block detection and response. The third-party services use active deception and other sophisticated tactics to detect ad-blockers. We also find that the third-party services can successfully circumvent ad-blockers and display ads on publisher websites. Finally, we design and implement a proof-of-concept stealthy ad-blocker that can circumvent state-of-the-art ad-block detectors.