Privacy in the Web has become a major concern resulting in the popular use of various tools for blocking tracking services. Most of these tools rely on manually maintained blacklists, which need to be kept up-to-date to protect Web users’ privacy efficiently. It is challenging to keep pace with today’s quickly evolving advertisement and analytics landscape. In order to support blacklist maintainers with this task, we identify a set of Web traffic features for identifying privacyintrusive services. Based on these features, we develop an automatic approach that learns the properties of advertisement and analytics services listed by existing blacklists and proposes new services for inclusion on blacklists. We evaluate our technique on real traffic traces of a campus network and find in the order of 200 new privacy-intrusive Web services that are not listed by the most popular Firefox plug-in Adblock Plus. The proposed Web traffic features are easy to derive, allowing a distributed implementation of our approach.

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a b s t r a c t HTTP and HTTPS traffic recorded at the perimeter of an organization is an exhaustive data source for the forensic investigation of security incidents. However, due to the nested nature of today's Web page structures, it is a huge manual effort to tell apart benign traffic caused by regular user browsing from malicious traffic that relates to malware or insider threats. We present Hviz, an interactive visualization approach to represent the event timeline of HTTP and HTTPS activities of a workstation in a comprehensible manner. Hviz facilitates incident investigation by structuring, aggregating, and correlating HTTP events between workstations in order to reduce the number of events that are exposed to an investigator while preserving the big picture. We have implemented a prototype system and have used it to evaluate its utility using synthetic and real-world HTTP traces from a campus network. Our results show that Hviz is able to significantly reduce the number of user browsing events that need to be exposed to an investigator by distilling the structural properties of HTTP traffic, thus simplifying the examination of malicious activities that arise from malware traffic or insider threats.

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