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Welk, A., Zielinska, O., Tembe, R., Xe, G., Hong, K. W., Murphy-Hill, E., Mayhorn, C. B..  In Press.  Will the “Phisher-men” Reel you in? Assessing Individual Differences in a Phishing Detection Task International Journal of Cyber Behavior, Psychology, and Learning. .

Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

Mohammadi, M., Chu, B., Lipford, H. R., Murphy-Hill, E..  2016.  Automatic Web Security Unit Testing: XSS Vulnerability Detection. 2016 IEEE/ACM 11th International Workshop in Automation of Software Test (AST). :78–84.

Integrating security testing into the workflow of software developers not only can save resources for separate security testing but also reduce the cost of fixing security vulnerabilities by detecting them early in the development cycle. We present an automatic testing approach to detect a common type of Cross Site Scripting (XSS) vulnerability caused by improper encoding of untrusted data. We automatically extract encoding functions used in a web application to sanitize untrusted inputs and then evaluate their effectiveness by automatically generating XSS attack strings. Our evaluations show that this technique can detect 0-day XSS vulnerabilities that cannot be found by static analysis tools. We will also show that our approach can efficiently cover a common type of XSS vulnerability. This approach can be generalized to test for input validation against other types injections such as command line injection.