CCE Theses and Dissertations

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


College of Computing and Engineering


Yair Levy

Committee Member

Wei Li

Committee Member

Ajoy Kumar


cybersecurity, distracting environments, judgment error in cybersecurity, phishing e-mail mitigation, social engineering


Phishing continues to be an invasive threat to computer and mobile device users. Cybercriminals continuously develop new phishing schemes using e-mail and malicious search engine links to gather the personal information of unsuspecting users. This information is used for financial gains through identity theft schemes or draining victims' financial accounts. Many users of varying demographic backgrounds fall victim to phishing schemes at one time or another. Users are often distracted and fail to process the phishing attempts fully, then unknowingly fall victim to the scam until much later. Users operating mobile phones and computers are likely to make judgment errors when making decisions in distracting environments due to cognitive overload. Distracted users cannot distinguish between legitimate and malicious emails or search engine results correctly. Mobile phone users can have a harder time distinguishing malicious content due to the smaller screen size and the limited security features in mobile phone applications.

The main goal of this research study was to design, develop, and validate experimental settings to empirically test if there are significant mean differences in users’ judgment when: exposed to two types of simulated social engineering attacks (phishing & Potentially Malicious Search Engine Results (PMSER)), based on the interaction of the kind of environment (distracting vs. non-distracting) and type of device used (mobile vs. computer). This research used field experiments to test whether users are more likely to fall for phishing schemes in a distracting environment while using mobile phones or desktop/laptop computers. The second phase included a pilot test with 10 participants testing the Subject Matter Experts (SME) validated tasks and measures. The third phase included the delivery of the validated tasks and measures that were revised through the pilot testing phase with 68 participants.

The results of the first phase have SME validated two sets of experimental tasks and eight experimental protocols to assess the measures of users’ judgment when exposed to two types of simulated social engineering attacks (phishing & PMSER) in two kinds of environments (distracting vs. non-distracting) and two types of devices (mobile phone vs. computer). The second phase results, the phishing mini-IQ test results, do not follow what was initially indicated in prior literature. Specifically, it was surprising to learn that the non-distracting environment results for the Phishing IQ tests were overall lower than those of distracting environment, which is counter to what was envisioned. These Phishing IQ test results may be assumed to be because, during the distracting environment, the participants were monitored over zoom to enable the distracting sound file. In contrast, in the non-distracting environment, they have marked the selections independently and may have rushed to identify the phishing samples. In contrast, PMSER detection on a computer outperformed mobile devices. It is suspected that these results are more accurate as individuals’ familiarity with PMSER is much lower. Their habituation to such messages is more deficient, causing them to pay closer attention and be more precise in their detections. A two-way Analysis of Variance (ANOVA) was conducted on the results. While it appears that some variations do exist, none of the comparisons were significant for Phishing IQ tests by environment (F=3.714, p=0.061) or device type (F=0.380, p=0.541), and PMSER IQ tests by environment (F=1.383, p=0.247) or device type (F=0.228, p=0.636). The results for the final phase showed there were no significant differences among both groups for Phishing and PMSER (F=0.985, p=0.322) and PMSER (F=3.692, p=0.056) using a two-way ANOVA. The two-way ANOVA results also showed significant differences among both groups for Phishing and PMSER vs. Device Type and Environment, Phishing (F=3.685, p=0.013), PMSER (F=1.629, p=0.183). A two-way ANOVA was evaluated for significant differences between groups. The results of the two-way ANOVA showed there were significant differences among both groups for Phishing and PMSER vs. Device Type and Environment. Phishing (F=3.685, p=0.013), PMSER (F=1.629, p=0.183). The p-values of the F-test for the Phishing IQ vs. Device Type and Environment were lower than the .05 level of significance. The two-way Analysis of Covariance (ANCOVA) results showed significant differences between Phishing vs. Environment and Device Type plus PMSER vs. Environment and Device Type. Specifically, the Education covariate for Table 32(F=3.930, p=0.048), Table 33(F=3.951, p=0.048), Table 34(F=10.429, p=0.001), and Table 35(F=10.329, p=0.001) was lower than the .05 level of significance.