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Journal Articles AI Communications Year : 2015

REPLOT: REtrieving Profile Links On Twitter for malicious campaign discovery

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Abstract

Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns.

Dates and versions

hal-02274660 , version 1 (30-08-2019)

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Cite

Charles Perez, B. Birregah, Robert Layton, Marc Lemercier, Paul Watters. REPLOT: REtrieving Profile Links On Twitter for malicious campaign discovery. AI Communications, 2015, 29 (1), pp.107-122. ⟨10.3233/AIC-150659⟩. ⟨hal-02274660⟩
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