Proceedings of the 13th international conference on World Wide Web
Journal of the American Society for Information Science and Technology
The effects of restrictions on number of connections in OSNs: a case-study on twitter
WOSN'10 Proceedings of the 3rd conference on Online social networks
Detecting spam bots in online social networking sites: a machine learning approach
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
Authorship Attribution for Twitter in 140 Characters or Less
CTC '10 Proceedings of the 2010 Second Cybercrime and Trustworthy Computing Workshop
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Toward worm detection in online social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Security Issues in Online Social Networks
IEEE Internet Computing
SPOT 1.0: Scoring Suspicious Profiles on Twitter
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
The socialbot network: when bots socialize for fame and money
Proceedings of the 27th Annual Computer Security Applications Conference
A weighted profile intersection measure for profile-based authorship attribution
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Detecting collective attention spam
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Understanding and combating link farming in the twitter social network
Proceedings of the 21st international conference on World Wide Web
An MCL-Based Approach for Spam Profile Detection in Online Social Networks
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
Detecting Spam and Promoting Campaigns in the Twitter Social Network
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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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.