Web Tracking Site Detection Based on Temporal Link Analysis

Web tracking sites or Web bugs are potential but serious threats to users' privacy during Web browsing. Web sites and their associated advertising sites surreptitiously gather the profiles of visitors and possibly abuse or improperly expose them, even if visitors do not provide their profiles consciously. In order to prevent such activities in a corporate network, most companies employ filters that rely on blacklists, however, these lists are insufficient. In this paper, we propose Web tracking sites detection and blacklist generation based on temporal link analysis. Our proposal analyzes traffic at the network gateway so that it can monitor all tracking sites in the administrative network. The proposed algorithm constructs a graph between sites and their visited time in order to characterize each site. Then, the system classifies suspicious sites using machine-learning algorithms. We confirm that 62-73% blacklisted sites are detected by the proposed system, and 96% of unlisted sites are unknown or suspicious tracking sites.