Authors
Charles Perez, Babiga Birregah, Robert Layton, Marc Lemercier, Paul Watters,
Title
REPLOT: REtrieving Profile Links On Twitter for suspicious networks detection
In
Proceedings of the 2013 International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Pages
8 p
Publisher
IEEE
Year
2013
Indexed by
Abstract
In the last few decades social networking sites have encountered their first large-scale security issues. The high number of users associated with the presence of sensitive data (personal or professional) is certainly an unprecedented opportunity for malicious activities. As a result, one observes that malicious users have are progressively turning their attention from traditional e-mail to online social networks to carry out their attacks. Moreover, it is now observed that attacks are not only performed by individual profiles, but that on a larger scale, a set of profiles can act in coordination in making such attacks. The latter are referred to as malicious social campaigns. In this paper, we present a novel approach that combines authorship attribution techniques with a behavioural analysis for detecting and characterizing social campaigns. The proposed approach is performed in three steps: first, suspicious profiles are identified from a behavioural analysis; second, connections between suspicious profiles are retrieved using a combination of authorship attribution and temporal similarity; third, a clustering algorithm is performed to identify and characterize the suspicious campaigns obtained. We provide a real-life application of the methodology on a set of 1,000 suspicious Twitter profiles tracked over a period of forty days. Our results show that a large set of suspicious profiles behave in coordination (70%) and propagate mainly, but not only, trustworthy URLs on the online social network. Among the three largest detected campaigns, we have highlighted that one represents an important security issue for the platform by promoting a significant set of malicious URLs.
Affiliations
Offprint