The rise of adblockers is creating lots of concerns to the online content publishing industry, as it severely affects the possibility to offer free-content to end-users by subsidizing the fruition costs with advertisements.While many detection techniques have been proposed as a countermeasure to the diffusion of adblocks, they either rely on the injection of code in the served web pages, or require to perform passive measurements for a long time, thus leading to high costs and delays before collecting the desired information.Motivated by these reasons, in this paper we propose a novel technique to conduct in-network adblock usage measurements, inspecting only few minutes of network traffic. Our approach relies on network traffic inspection, and classification with machine learning techniques to detect whether the user is blocking, or not, the advertisements.Key findings obtained show that by inspecting only few minutes of network traffic, we can reliably perform the detection with an accuracy up to 99%, with a negligible computational overhead.

Catching free-riders: in-network adblock detection with machine learning techniques

Moro, D;Capone, A
2018-01-01

Abstract

The rise of adblockers is creating lots of concerns to the online content publishing industry, as it severely affects the possibility to offer free-content to end-users by subsidizing the fruition costs with advertisements.While many detection techniques have been proposed as a countermeasure to the diffusion of adblocks, they either rely on the injection of code in the served web pages, or require to perform passive measurements for a long time, thus leading to high costs and delays before collecting the desired information.Motivated by these reasons, in this paper we propose a novel technique to conduct in-network adblock usage measurements, inspecting only few minutes of network traffic. Our approach relies on network traffic inspection, and classification with machine learning techniques to detect whether the user is blocking, or not, the advertisements.Key findings obtained show that by inspecting only few minutes of network traffic, we can reliably perform the detection with an accuracy up to 99%, with a negligible computational overhead.
2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1146186
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