Indirect information linkage for OSINT through authorship analysis of aliases

Robert Layton; Charles Perez; Babiga Birregah; Paul Watters; Marc Lemercier

doi:10.1007/978-3-642-40319-4_4

Indirect information linkage for OSINT through authorship analysis of aliases

Robert Layton, Charles Perez, Babiga Birregah, Paul Watters, Marc Lemercier

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Trends and applications in knowledge discovery and data mining
Subtitle of host publication	PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers
Editors	Jiuyong Li, Longbing Cao, Can Wang, Kay Chen Tan, Bo Liu, Jian Pei, Vincent S. Tseng
Place of Publication	Berlin ; Heidelberg
Publisher	Springer, Springer Nature
Pages	36-46
Number of pages	11
ISBN (Electronic)	9783642403194
ISBN (Print)	9783642403187
DOIs	https://doi.org/10.1007/978-3-642-40319-4_4
Publication status	Published - 2013
Externally published	Yes
Event	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia Duration: 14 Apr 2013 → 17 Apr 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	7867
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/Territory	Australia
City	Gold Coast, QLD
Period	14/04/13 → 17/04/13

Access to Document

10.1007/978-3-642-40319-4_4

Cite this

Layton, R., Perez, C., Birregah, B., Watters, P., & Lemercier, M. (2013). Indirect information linkage for OSINT through authorship analysis of aliases. In J. Li, L. Cao, C. Wang, K. C. Tan, B. Liu, J. Pei, & V. S. Tseng (Eds.), Trends and applications in knowledge discovery and data mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers (pp. 36-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867). Springer, Springer Nature. https://doi.org/10.1007/978-3-642-40319-4_4

Layton, Robert ; Perez, Charles ; Birregah, Babiga et al. / Indirect information linkage for OSINT through authorship analysis of aliases. Trends and applications in knowledge discovery and data mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers. editor / Jiuyong Li ; Longbing Cao ; Can Wang ; Kay Chen Tan ; Bo Liu ; Jian Pei ; Vincent S. Tseng. Berlin ; Heidelberg : Springer, Springer Nature, 2013. pp. 36-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Indirect information linkage for OSINT through authorship analysis of aliases",

abstract = "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.",

author = "Robert Layton and Charles Perez and Babiga Birregah and Paul Watters and Marc Lemercier",

year = "2013",

doi = "10.1007/978-3-642-40319-4_4",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Layton, R, Perez, C, Birregah, B, Watters, P & Lemercier, M 2013, Indirect information linkage for OSINT through authorship analysis of aliases. in J Li, L Cao, C Wang, KC Tan, B Liu, J Pei & VS Tseng (eds), Trends and applications in knowledge discovery and data mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7867, Springer, Springer Nature, Berlin ; Heidelberg, pp. 36-46, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD, Australia, 14/04/13. https://doi.org/10.1007/978-3-642-40319-4_4

Indirect information linkage for OSINT through authorship analysis of aliases. / Layton, Robert; Perez, Charles; Birregah, Babiga et al.
Trends and applications in knowledge discovery and data mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers. ed. / Jiuyong Li; Longbing Cao; Can Wang; Kay Chen Tan; Bo Liu; Jian Pei; Vincent S. Tseng. Berlin ; Heidelberg: Springer, Springer Nature, 2013. p. 36-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

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AU - Watters, Paul

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AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer, Springer Nature

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Layton R, Perez C, Birregah B, Watters P, Lemercier M. Indirect information linkage for OSINT through authorship analysis of aliases. In Li J, Cao L, Wang C, Tan KC, Liu B, Pei J, Tseng VS, editors, Trends and applications in knowledge discovery and data mining: PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD: revised selected papers. Berlin ; Heidelberg: Springer, Springer Nature. 2013. p. 36-46. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40319-4_4

Indirect information linkage for OSINT through authorship analysis of aliases

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