Demo Data from VLDB 2026 / reviewers in the wild / expert
Kobi Boymgold
dblp:243/5870
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
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 · 70% Systems and software security · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Usable security › security education
security awareness |
0.4 | 1 | 2020 | Evaluating the Information Security Awareness of Smartphone Users · CHI 2020 |
Usable security
social engineering |
0.4 | 1 | 2020 | Evaluating the Information Security Awareness of Smartphone Users · CHI 2020 |
Systems and software security › vulnerability management
vulnerability assessment |
0.4 | 1 | 2020 | Evaluating the Information Security Awareness of Smartphone Users · CHI 2020 |
Usable security
user behavior |
0.1 | 1 | 2020 | Evaluating the Information Security Awareness of Smartphone Users · CHI 2020 |
Methods — techniques the papers use, named apart from their topics
user study · 0.4questionnaire · 0.4network traffic monitoring · 0.4mobile agents · 0.4cybersecurity challenges · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Evaluating the Information Security Awareness of Smartphone UsersabstractInformation security awareness (ISA) is a practice focused on the set of skills which help a user successfully mitigate social engineering (SE) attacks. Evaluating the ISA of users is crucial, since early identification of users who are more vulnerable to SE attacks improves system security. Previous studies for evaluating the ISA of smartphone users rely on subjective data sources (questionnaires) and do not address the differences between classes of SE attacks. This paper presents a framework for evaluating the ISA of smartphone users for specific attack classes. In addition to questionnaires, we utilize objective data sources: a mobile agent, a network traffic monitor, and cybersecurity challenges. We evaluated the framework by conducting a long-term user study involving 162 users. The results show that: the self-reported behavior of users differs significantly from their actual behavior and the ISA level derived from the actual behavior of users is highly correlated with their ability to mitigate SE attacks. Ron Biton, Kobi Boymgold, Rami Puzis, Asaf Shabtai |
CHI | 2 |