9,489 research outputs found
XSS-FP: Browser Fingerprinting using HTML Parser Quirks
There are many scenarios in which inferring the type of a client browser is
desirable, for instance to fight against session stealing. This is known as
browser fingerprinting. This paper presents and evaluates a novel
fingerprinting technique to determine the exact nature (browser type and
version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks
exercised through XSS. Our experiments show that the exact version of a web
browser can be determined with 71% of accuracy, and that only 6 tests are
sufficient to quickly determine the exact family a web browser belongs to
Automated Website Fingerprinting through Deep Learning
Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.Comment: To appear in the 25th Symposium on Network and Distributed System
Security (NDSS 2018
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
- …