Visible to the public Biblio

Filters: Author is Christopher B. Mayhorn  [Clear All Filters]
2016-04-10
Olga A. Zielinska, Allaire K. Welk, Emerson Murphy-Hill, Christopher B. Mayhorn.  2016.  A temporal analysis of persuasion principles in phishing emails. Human Factors and Ergonomics Society 60th Annual Meeting.

Eight hundred eighty-seven phishing emails from Arizona State University, Brown University, and Cornell University were assessed by two reviewers for Cialdini’s six principles of persuasion: authority, social proof, liking/similarity, commitment/consistency, scarcity, and reciprocation. A correlational analysis of email characteristics by year revealed that the persuasion principles of commitment/consistency and scarcity have increased over time, while the principles of reciprocation and social proof have decreased over time. Authority and liking/similarity revealed mixed results with certain characteristics increasing and others decreasing. Results from this study can inform user training of phishing emails and help cybersecurity software to become more effective. 

2016-01-09
2015-07-06
2015-04-02
Allaire K. Welk, Christopher B. Mayhorn.  2015.  All Signals Go: Investigating How Individual Differences Affect Performance on a Medical Diagnosis Task Designed to Parallel a Signal Intelligence Analyst Task. Symposium and Bootcamp on the Science of Security (HotSoS).

Signals intelligence analysts play a critical role in the United States government by providing information regarding potential national security threats to government leaders. Analysts perform complex decision-making tasks that involve gathering, sorting, and analyzing information. The current study evaluated how individual differences and training influence performance on an Internet search-based medical diagnosis task designed to simulate a signals analyst task. The implemented training emphasized the extraction and organization of relevant information and deductive reasoning. The individual differences of interest included working memory capacity and previous experience with elements of the task, specifically health literacy, prior experience using the Internet, and prior experience conducting Internet searches. Preliminary results indicated that the implemented training did not significantly affect performance, however, working memory significantly predicted performance on the implemented task. These results support previous research and provide additional evidence that working memory capacity influences performance on cognitively complex decision-making tasks, whereas experience with elements of the task may not. These findings suggest that working memory capacity should be considered when screening individuals for signals intelligence positions. Future research should aim to generalize these findings within a broader sample, and ideally utilize a task that directly replicates those performed by signals analysts.

Olga Zielinska, Allaire Welk, Christopher B. Mayhorn, Emerson Murphy-Hill.  2015.  Exploring expert and novice mental models of phishing. HotSoS: Symposium and Bootcamp on the Science of Security.

Experience influences actions people take in protecting themselves against phishing. One way to measure experience is through mental models. Mental models are internal representations of a concept or system that develop with experience. By rating pairs of concepts on the strength of their relationship, networks can be created through Pathfinder, showing an in-depth analysis of how information is organized. Researchers had novice and expert computer users rate three sets of terms related to phishing. The terms were divided into three categories: prevention of phishing, trends and characteristics of phishing attacks, and the consequences of phishing. Results indicated that expert mental models were more complex with more links between concepts. Specifically, experts had sixteen, thirteen, and fifteen links in the networks describing the prevention, trends, and consequences of phishing, respectively; however, novices only had eleven, nine, and nine links in the networks describing prevention, trends, and consequences of phishing, respectively. These preliminary results provide quantifiable network displays of mental models of novices and experts that cannot be seen through interviews. This information could provide a basis for future research on how mental models could be used to determine phishing vulnerability and the effectiveness of phishing training.