Indirect information linkage for OSINT through authorship analysis of aliases
- Authors: Layton, Robert , Perez, Charles , Birregah, Babiga , Watters, Paul , Lemercier, Marc
- Date: 2013
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining Vol. 7867 LNAI, p. 36-46
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- Description: In this paper we examine the problem of automatically linking online accounts for open source intelligence gathering. We specifically aim to determine if two social media accounts are shared by the same author, without the use of direct linking evidence. We profile the accounts using authorship analysis and find the best matching guess. We apply this to a series of Twitter accounts identified as malicious by a methodology named SPOT and find several pairs of accounts that belong to the same author, despite no direct evidence linking the two. Overall, our results show that linking aliases is possible with an accuracy of 84%, and using our automated threshold method improves our accuracy to over 90% by removing incorrectly discovered matches. © Springer-Verlag 2013.
REPLOT: REtrieving profile links on Twitter for suspicious networks detection
- Authors: Perez, Charles , Birregah, Babiga , Layton, Robert , Lemercier, Marc , Watters, Paul
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 p. 1307-1314
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- Description: 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.
REPLOT : REtrieving Profile Links on Twitter for malicious campaign discovery
- Authors: Perez, Charles , Birregah, Babiga , Layton, Robert , Lemercier, Marc , Watters, Paul
- Date: 2015
- Type: Text , Journal article
- Relation: AI Communications Vol. 29, no. 1 (2015), p. 107-122
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- Description: 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. © 2016 - IOS Press and the authors. All rights reserved.