- Title
- REPLOT: REtrieving profile links on Twitter for suspicious networks detection
- Creator
- Perez, Charles; Birregah, Babiga; Layton, Robert; Lemercier, Marc; Watters, Paul
- Date
- 2013
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/62094
- Identifier
- vital:5754
- Identifier
- ISBN:9781450322409 (ISBN)
- 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 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 characterise the suspicious campaigns obtained. We provide a real-life application of the methodology on a sample of 1,000 suspicious Twitter profiles tracked over a period of forty days. Our results show that a large set of suspicious profiles behaves in coordination (70%) and propagates 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. Copyright 2013 ACM.
- Publisher
- Niagara Falls, ON; Canada
- Relation
- Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 p. 1307-1314
- Rights
- This metadata is freely available under a CCO license
- Subject
- Authorship attribution; Clustering; Malicious campaigns; Online social networks; Suspicious profiles; Twitter
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