Demo Data from VLDB 2026 / reviewers in the wild / expert
Steve Sheng
dblp:23/3412
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 2 first-authorComputer networks · 1Security and privacy · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Usable security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Usable security › social engineering
phishing susceptibility |
0.1 | 1 | 2010 | Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions · CHI 2010 |
Usable security
security education |
0.1 | 1 | 2010 | Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions · CHI 2010 |
Collaborative and social computing
crowdsourcing |
0.0 | 1 | 2010 | Are your participants gaming the system?: screening mechanical turk workers · CHI 2010 |
Usable security › authentication usability
user authentication behavior |
0.0 | 1 | 2010 | Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions · CHI 2010 |
Methods — techniques the papers use, named apart from their topics
survey · 0.2screening questions · 0.2roleplay survey · 0.1mediation analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Are your participants gaming the system?: screening mechanical turk workersabstractIn this paper we discuss a screening process used in conjunction with a survey administered via Amazon.com's Mechanical Turk. We sought an easily implementable method to disqualify those people who participate but don't take the study tasks seriously. By using two previously pilot tested screening questions, we identified 764 of 1,962 people who did not answer conscientiously. Young men seem to be most likely to fail the qualification task. Those that are professionals, students, and non-workers seem to be more likely to take the task seriously than financial workers, hourly workers, and other workers. Men over 30 and women were more likely to answer seriously. Julie S. Downs, Mandy B. Holbrook, Steve Sheng, Lorrie Faith Cranor |
CHI | 3 |
| 2010 | Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventionsabstractIn this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links. Steve Sheng, Mandy B. Holbrook, Ponnurangam Kumaraguru, Lorrie Faith Cranor, Julie S. Downs |
CHI | 1 |
| 2010 | Teaching Johnny not to fall for phishabstractPhishing attacks, in which criminals lure Internet users to Web sites that spoof legitimate Web sites, are occurring with increasing frequency and are causing considerable harm to victims. While a great deal of effort has been devoted to solving the phishing problem by prevention and detection of phishing emails and phishing Web sites, little research has been done in the area of training users to recognize those attacks. Our research focuses on educating users about phishing and helping them make better trust decisions. We identified a number of challenges for end-user security education in general and anti-phishing education in particular: users are not motivated to learn about security; for most users, security is a secondary task; it is difficult to teach people to identify security threats without also increasing their tendency to misjudge nonthreats as threats. Keeping these challenges in mind, we developed an email-based anti-phishing education system called “PhishGuru” and an online game called “Anti-Phishing Phil” that teaches users how to use cues in URLs to avoid falling for phishing attacks. We applied learning science instructional principles in the design of PhishGuru and Anti-Phishing Phil. In this article we present the results of PhishGuru and Anti-Phishing Phil user studies that demonstrate the effectiveness of these tools. Our results suggest that, while automated detection systems should be used as the first line of defense against phishing attacks, user education offers a complementary approach to help people better recognize fraudulent emails and websites. Ponnurangam Kumaraguru, Steve Sheng, Alessandro Acquisti, Lorrie Faith Cranor, Jason I. Hong |
ACM Trans. Internet Techn. | 2 |
| 2007 | Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phishabstractIn this paper we describe the design and evaluation of Anti-Phishing Phil, an online game that teaches users good habits to help them avoid phishing attacks. We used learning science principles to design and iteratively refine the game. We evaluated the game through a user study: participants were tested on their ability to identify fraudulent web sites before and after spending 15 minutes engaged in one of three anti-phishing training activities (playing the game, reading an anti-phishing tutorial we created based on the game, or reading existing online training materials). We found that the participants who played the game were better able to identify fraudulent web sites compared to the participants in other conditions. We attribute these effects to both the content of the training messages presented in the game as well as the presentation of these materials in an interactive game format. Our results confirm that games can be an effective way of educating people about phishing and other security attacks. Steve Sheng, Bryant Magnien, Ponnurangam Kumaraguru, Alessandro Acquisti, Lorrie Faith Cranor, Jason I. Hong, Elizabeth Ferrall-Nunge |
SOUPS | 1 |