TY - GEN
T1 - Multimodal data fusion and behavioral analysis tooling for exploring trust, trust-propensity, and phishing victimization in online environments
AU - Hefley, Mickey
AU - Wethor, Gabrielle E.
AU - Hale, Matthew L.
N1 - Publisher Copyright:
© 2018 IEEE Computer Society. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Online environments, including email and social media platforms, are continuously threatened by malicious content designed by attackers to install malware on unsuspecting users and/or phish them into revealing sensitive data about themselves. Often slipping past technical mitigations (e.g. spam filters), attacks target the human element and seek to elicit trust as a means of achieving their nefarious ends. Victimized end-users lack the discernment, visual acuity, training, and/or experience to correctly identify the nefarious antecedents of trust that should prompt suspicion. Existing literature has explored trust, trust-propensity, and victimization, but studies lack data capture richness, realism, and/or the ability to investigate active user interactions. This paper defines a data collection and fusion approach alongside new open-sourced behavioral analysis tooling that addresses all three factors to provide researchers with empirical, evidence-based, insights into active end-user trust behaviors. The approach is evaluated in terms of comparative analysis, run-time performance, and fused data accuracy.
AB - Online environments, including email and social media platforms, are continuously threatened by malicious content designed by attackers to install malware on unsuspecting users and/or phish them into revealing sensitive data about themselves. Often slipping past technical mitigations (e.g. spam filters), attacks target the human element and seek to elicit trust as a means of achieving their nefarious ends. Victimized end-users lack the discernment, visual acuity, training, and/or experience to correctly identify the nefarious antecedents of trust that should prompt suspicion. Existing literature has explored trust, trust-propensity, and victimization, but studies lack data capture richness, realism, and/or the ability to investigate active user interactions. This paper defines a data collection and fusion approach alongside new open-sourced behavioral analysis tooling that addresses all three factors to provide researchers with empirical, evidence-based, insights into active end-user trust behaviors. The approach is evaluated in terms of comparative analysis, run-time performance, and fused data accuracy.
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M3 - Conference contribution
AN - SCOPUS:85108285759
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 862
EP - 871
BT - Proceedings of the 51st Annual Hawaii International Conference on System Sciences, HICSS 2018
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 51st Annual Hawaii International Conference on System Sciences, HICSS 2018
Y2 - 2 January 2018 through 6 January 2018
ER -