15,079 research outputs found

    Towards the characterization of individual users through Web analytics

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    We perform an analysis of the way individual users navigate in the Web. We focus primarily in the temporal patterns of they return to a given page. The return probability as a function of time as well as the distribution of time intervals between consecutive visits are measured and found to be independent of the level of activity of single users. The results indicate a rich variety of individual behaviors and seem to preclude the possibility of defining a characteristic frequency for each user in his/her visits to a single site.Comment: 8 pages, 4 figures. To appear in Proceeding of Complex'0

    Characterizing web pornography consumption from passive measurements

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    Web pornography represents a large fraction of the Internet traffic, with thousands of websites and millions of users. Studying web pornography consumption allows understanding human behaviors and it is crucial for medical and psychological research. However, given the lack of public data, these works typically build on surveys, limited by different factors, e.g. unreliable answers that volunteers may (involuntarily) provide. In this work, we collect anonymized accesses to pornography websites using HTTP-level passive traces. Our dataset includes about 15 00015\,000 broadband subscribers over a period of 3 years. We use it to provide quantitative information about the interactions of users with pornographic websites, focusing on time and frequency of use, habits, and trends. We distribute our anonymized dataset to the community to ease reproducibility and allow further studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages, 7 figure

    On participatory service provision at the network edge with community home gateways

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    Edge computing is considered as a technology to enable new types of services which operate at the network edge. There are important use cases in ambient intelligence and the Internet of Things (IoT) for edge computing driven by huge business potentials. Most of today's edge computing platforms, however, consist of proprietary gateways, which are either closed or fairly restricted to deploy any third-party services. In this paper we discuss a participatory edge computing system running on home gateways to serve as an open environment to deploy local services. We present first motivating use cases and review existing approaches and design considerations for the proposed system. Then we show our platform which materializes the principles of an open and participatory edge environment, to lower the entry barriers for service deployment at the network edge. By using containers, our platform can flexibly enable third-party services, and may serve as an infrastructure to support several application domains of ambient intelligence.Peer ReviewedPostprint (author's final draft

    Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

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    Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics
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