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

Date
2018-01-03
Authors
Hefley, Michael
Wethor, Gabrielle
Hale, Matthew L.
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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.
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Keywords
Data Analytics in Behavioral Research, Phishing, Data Fusion, Trust, Eye Tracker, Behavioral Analysis
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