2014 IEEE Security and Privacy Workshops 2014
DOI: 10.1109/spw.2014.39
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Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

Abstract: Abstract-Organizations often suffer harm from individuals who bear no malice against them but whose actions unintentionally expose the organizations to risk-the unintentional insider threat (UIT). In this paper we examine UIT cases that derive from social engineering exploits. We report on our efforts to collect and analyze data from UIT social engineering incidents to identify possible behavioral and technical patterns and to inform future research and development of UIT mitigation strategies.

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Cited by 69 publications
(49 citation statements)
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“…The SE requests that the target navigates to a web address and enter confidential information [40,41].…”
Section: Bidirectional Communication -Templatementioning
confidence: 99%
See 1 more Smart Citation
“…The SE requests that the target navigates to a web address and enter confidential information [40,41].…”
Section: Bidirectional Communication -Templatementioning
confidence: 99%
“…The malware masked itself on systems and was designed to erase itself if it tried to compromise a system and was unsuccessful [40,41].…”
Section: Unidirectional Communication -Templatementioning
confidence: 99%
“…The study focused on IT departments and a more abstract view of SEAs without considering SEAs concepts related to critical human factors and their relationships to the concept of SI. Greitzer et al (2014) looked at the insider threat that derives from SEAs [21]. The study considered some related human factors but concentrated mainly on unintentional insider threats whilst observing psychological and social characteristic of people.…”
Section: Social Engineering Attacks Taxonomymentioning
confidence: 99%
“…The Insider threat patterns provided by CERT [6] use the System Dynamics models, which can express dependencies between variables. The System Dynamics approach has also been successfully applied in other approaches to Insider threats, for example, in the modeling of unintentional insider threats [11]. Axelrad et al [2] have used Bayesian networks for modelling Insider threats in particular the human disposition.…”
Section: Related Work and Conclusionmentioning
confidence: 99%