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PRODID:Linklings LLC
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TZID:America/Phoenix
X-LIC-LOCATION:America/Phoenix
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TZOFFSETFROM:-0700
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TZNAME:MST
DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203101Z
LOCATION:FLW Salon I
DTSTART;TZID=America/Phoenix:20240910T100000
DTEND;TZID=America/Phoenix:20240910T101500
UID:HFESAM_ASPIRE - Presented by HFES_sess130_LECT599@linklings.com
SUMMARY:Agreement and Divergence in Predicting Human Phishing Classificati
 on Decisions
DESCRIPTION:Lecture\n\nShova Kuikel (University of Texas at El Paso), Mari
 a José Ferreira (Carnegie Mellon University), Kuldeep Singh (University of
  Texas at El Paso), Palvi Aggarwal (The University of Texas at El Paso), a
 nd Cleotilde Gonzalez (Carnegie Mellon University)\n\nDespite enormous eff
 orts to develop defenses against phishing attacks, humans still struggle t
 o detect phishing emails given the constantly evolving attacker strategies
 . This paper aims to test the predictive capabilities of a cognitive model
  that represents the individual susceptibility to phishing emails. We deve
 loped an instance-based learning model (IBL) that captures the frequency, 
 recency, and familiarity aspects of decision-making and explores its poten
 tial for personalized anti-phishing training. We investigate the same cogn
 itive model using three different methods. At the same time, we compare th
 e performance of these three models to human classification of phishing an
 d ham emails and determine the synchronization rates with human participan
 ts in a Phishing Training Task. Our results reveal that using prior human 
 experience and optimizing parameters improves model accuracy. These findin
 gs suggest significant advances in modeling human decision-making patterns
  and cognitive processes, demonstrating strong alignment with human decisi
 ons during training.\n\nTrack: Cybersecurity\n\nSession Chair: Prashanth R
 ajivan (University of Washington)
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