Date of Award
2018
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
Committee Chair
Feng Zhu
Committee Member
Mary Ellen Weisskopf
Committee Member
Harry Delugach
Committee Member
Huaming Zhang
Subject(s)
Human-computer interaction., Warnings--Design., Eye tracking., Computer security.
Abstract
People experience many cybersecurity attacks in their daily lives. They may also unnecessarily disclose private information. Computer warnings are used as one of the last lines of defense. Researchers and designers have actively sought to understand how users interact with security warnings and why warnings are often ignored. They want to design warnings to be effective but not interrupt people’s main tasks. In this dissertation, I propose a framework named the Leveled Human Behavior Warning (LHBW) model. We group computer warning designs and evaluations into five levels: warning stimuli, warning perception, warning storage and memory retrieval, decision-making, and behavior. The LHBW model focus on providing approaches to help computer warning designers enhance their warnings. We analyze existing warning approaches using the LHBW model. Specifically, we study warning stimuli, perception factors, long-term memory, decision-making, and types of behavior. Based on the LHBW model, we designed two kinds of warnings – dynamic supraliminal warnings and subliminal warnings. By using eye-gaze information as input, the dynamic supraliminal warnings are shown “at the right time and at the right place.” The warning guides the user’s attention by popping up where the user is looking. We developed an Eye Tracking Information Analysis (ETIA) tool to determine the warning’s display time. The ETIA is also used to record participants’ eye gaze movements and to evaluate the effectiveness of the dynamic warnings. The subliminal warning approach displays warnings below the conscious awareness level. We developed an advanced version of the ETIA tool to verify whether users’ attention is in the area of subliminal primes. Both dynamic supraliminal and subliminal warnings effectively reduced users’ disclosure of identity information.
Recommended Citation
Zeng, Mini, "Dynamic cybersecurity warnings : subliminal and supraliminal approaches" (2018). Dissertations. 150.
https://louis.uah.edu/uah-dissertations/150