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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Indirect Information Linkage for OSINT through Authorship Analysis of Aliases
verfasst von : Robert Layton, Charles Perez, Babiga Birregah, Paul Watters, Marc Lemercier
Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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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.