Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/49995

Multimodal Data Fusion and Behavioral Analysis Tooling for Exploring Trust, Trust-propensity, and Phishing Victimization in Online Environments

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Title:Multimodal Data Fusion and Behavioral Analysis Tooling for Exploring Trust, Trust-propensity, and Phishing Victimization in Online Environments
Authors:Hefley, Michael
Wethor, Gabrielle
Hale, Matthew L.
Keywords:Data Analytics in Behavioral Research
Phishing, Data Fusion, Trust, Eye Tracker, Behavioral Analysis
Date Issued:03 Jan 2018
Abstract: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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/49995
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.108
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics in Behavioral Research


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