Social Fingerprinting: Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling
- 30 June 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Dependable and Secure Computing
- Vol. 15 (4), 561-576
- https://doi.org/10.1109/TDSC.2017.2681672
Abstract
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 a 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 also evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection showing the superiority of our solution. Finally, 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.Funding Information
- H2020 Research Infrastructures (654024 SoBigData: Social Mining & Big Data Ecosyst)
- Fondazione Cassa di Risparmio di Lucca (IIT-0007044 Reviewland)
- Ministero dell Istruzione dell Universita e della Ricerca (PAR-FAS 2007-2013 SmartNews: Social sensing for Br)
- H2020 Marie Skłodowska-Curie Actions (675320 European Network of Excellence in Cybersecu)
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