Biblio

The Web ecosystem has been evolving over the past years and new Internet protocols, namely HTTP/2 over TLS/TCP and QUIC/UDP, are now used to deliver Web contents. Similarly, CDNs (Content Delivery Network) are deployed worldwide, caching contents close to end-users to optimize web browsing quality. We present in this paper an analysis of the influence of the Internet protocols and CDN on the Top 10,000 Alexa websites, based on a 12-month measurement campaign (from April 2018 to April 2019) performed via our tool Web View [1]. Part of our measurements are made public, represented on a monitoring website1, showing the results for the Top 50 Alexa Websites plus few specific websites and 8 french websites, suggested by the French Agency in charge of regulating telecommunications. Our analysis of this long-term measurement campaign allows to better analyze the delivery of public websites. For instance, it shows that even if some argue that QUIC optimizes the quality, it is not observed in the real-life since QUIC is not largely deployed. Our method for analyzing CDN delivery in the Web browsing allows us to evaluate its influence, which is important since their usage can decrease the web pages' loading time, on average 43.1% with HTTP/2 and 38.5% with QUIC, when requesting a second time the same home page.

Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.