Data Analysis and Modelling of Users' Behavior on the Web

The research developed during my PhD [1] was driven by the need to understand how people interact with the web. This information gives ISPs and network managers better visibility and understanding of how users and web services change over time. Thanks to traces and logs of users' traffic, my work focuses on two complementary aspects: (i) data analytics, and (ii) user modelling. In this work, I show how to reconstruct users' online activity from passive measurements and to model their behaviour. I introduce machine learning approaches to identify the intentionally visited web-pages and web-sites. I highlight device usage evolution, the structure of the navigation and the interactions with social networks and search engines. I build users' profiles and then I show how to re-identify users in a future time thanks to their behavioural fingerprints. This is also instrumental for security applications. I next study the interaction with online ads, capturing the impact of the temporal dynamics of shown advertisement and improving revenues. I make available all the anonymized datasets and code for the community, to guarantee results reproducibility and foster further analyses.