The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
It’s the deceiver and the receiver: Individual differences in phishing susceptibility and false positives with item profiling
Open accessAbstract
Phishing email is one of the biggest risks to online information security due to its ability to exploit human trust and naivety. Prior research has examined whether some people are more susceptible to phishing than others and what characteristics may predict this susceptibility. Given that there are no standardised measures or methodologies to detect phishing susceptibility, results have conflicted. To address this issue, the current study created a 40-item phishing detection task to measure both cognitive and behavioural indicators of phishing susceptibility and false positives (misjudged genuine email). The task is based on current real-life email stimuli (i.e., phishing and genuine) relevant to the student and general population. Extending previous literature we also designed a methodology for assessing phishing susceptibility by allowing participants to indicate perception of maliciousness of each email type and the actions they would take (keep it, trash it or seek further information). This enabled us to: (1) examine the relationships that psychological variables share with phishing susceptibility and false positives-both captured as consistent tendencies; (2) determine the relationships between perceptions of maliciousness with behavioural outcomes and psychological variables; and (3) determine the relationships between these tendencies and email characteristics. In our study, 150 undergraduate psychology students participated in exchange for partial course credit (98 Females; Mean age = 19.70, SD = 2.27). Participants also completed a comprehensive battery of psychometric tests assessing intelligence, pre- and on-task confidence, Big 6 personality, and familiarity/competence in computing and phishing. Results revealed that people showed distinct and robust tendencies for phishing susceptibility and false positives. A series of regression analyses looking at the accuracy of both phishing and false positives detection revealed that human-centred variables accounted for a good degree of variance in phishing susceptibility (about 54%), with perceptions of maliciousness, intelligence, knowledge of phishing, and on-task confidence contributing significantly, directly and/or indirectly via perception of maliciousness. A regression model looking at discriminating false positives has also shown that human-centred variables accounted for a reasonable degree of variance (41%), with perceptions of maliciousness, intelligence and on-task confidence contributing significantly, directly and/or indirectly via perception of maliciousness. Furthermore, the characteristics of the most effective phishing and misjudged genuine email items were profiled. Based on our findings, we suggest that future research should investigate these significant variables in more detail. We also recommend that future research should capture consistent response tendencies to determine vulnerability to phishing and false positives (rather than a one off response to a single email), and use the collection of the most current phishing email obtained from relevant sources to the population. It is important to capture perceptions of maliciousness of email because it is a key predictor of the action taken on the email. It directly predicts accuracy detection of phishing and genuine email, as well as mediating the relationships between some other predictors whose role would have been overlooked if the perceptions were not captured. The study provides the framework of human-centred variables which predict phishing and false positive susceptibility as well as the characteristics of email which most deceive people.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Unconscious influences on decision making: A critical review
Newell, B. R., & Shanks, D. R. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12003214 · Cited by 652
Flagged first, then by citations
Research works printed but not linked: 55
- TN Jagatic, 2007, Social phishing, Communications of the ACM, 50, 94, 10.1145/1290958.1290968 · DOI · Paper page
- S Abraham, 2010, An overview of social engineering malware: Trends, tactics, and implications, Technology in Society, 32, 183, 10.1016/j.techsoc.2010.07.001 · DOI
- RT Wright, 2014, Influence Techniques in Phishing Attacks: An Examination of Vulnerability and Resistance, Information Systems Research, 25, 385, 10.1287/isre.2014.0522 · DOI
- I Bose, 2014, Do phishing alerts impact global corporations? A firm value analysis, Decision Support Systems, 64, 67, 10.1016/j.dss.2014.04.006 · DOI
- TR Peltier, 2006, Social Engineering: Concepts and Solutions, The EDP Audit, Control, and Security Newsletter, 33, 1
- KW Hong, 2013, Keeping Up With The Joneses, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57, 1012, 10.1177/1541931213571226 · DOI
- M Alsharnouby, 2015, Why phishing still works: User strategies for combating phishing attacks, International Journal of Human-Computer Studies, 82, 69, 10.1016/j.ijhcs.2015.05.005 · DOI
- S Purkait, 2012, Phishing counter measures and their effectiveness–literature review, Information Management & Computer Security, 20, 382, 10.1108/09685221211286548 · DOI
- A Vance, 2018, Tuning Out Security Warnings: A Longitudinal Examination of Habituation Through fMRI, Eye Tracking, and Field Experiments, MIS Quarterly, 42, 355, 10.25300/MISQ/2018/14124 · DOI
- JS Downs, 2007, Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit, 37
- R Dhamija, 2006, CHI '06 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 581
- GD Moody, 2017, Which phish get caught? An exploratory study of individuals′ susceptibility to phishing, European Journal of Information Systems
- I Alseadoon, 2015, Proceedings of the 1st International Conference on Communication and Computer Engineering, 949
- T Halevi, 2015, Spear-Phishing in the Wild: A Real-World Study of Personality, Phishing Self-Efficacy and Vulnerability to Spear-Phishing Attacks, SSRN Electronic Journal
- A Vishwanath, 2011, Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model, Decision Support Systems, 51, 576, 10.1016/j.dss.2011.03.002 · DOI
- M Workman, 2008, Wisecrackers: A theory-grounded investigation of phishing and pretext social engineering threats to information security, Journal of the American Society for Information Science and Technology, 59, 662, 10.1002/asi.20779 · DOI
- K Parsons, 2013, 28th IFIP TC 11 International Conference: Security and Privacy Protection in Information Processing Systems, 366
- B Harrison, 2016, Individual processing of phishing emails, Online Information Review, 40, 265, 10.1108/OIR-04-2015-0106 · DOI
- J Wang, 2012, Research Article Phishing Susceptibility: An Investigation Into the Processing of a Targeted Spear Phishing Email, IEEE Transactions on Professional Communication, 55, 345, 10.1109/TPC.2012.2208392 · DOI
- SA Jackson, 2016, Decision Pattern Analysis as a General Framework for Studying Individual Differences in Decision Making, Journal of Behavioral Decision Making, 29, 392, 10.1002/bdm.1887 · DOI
- CI Canfield, 2016, Quantifying Phishing Susceptibility for Detection and Behavior Decisions, Human Factors: The Journal of the Human Factors and Ergonomics Society, 58, 1158, 10.1177/0018720816665025 · DOI
- M Pattinson, 2011, Managing Phishing Emails: A Scenario-Based Experiment Proceedings of the Fifth International Symposium on Human Aspects of Information Security & Assurance (HAISA)
- KS McGrew, 2009, CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research, Intelligence, 37, 1, 10.1016/j.intell.2008.08.004 · DOI · Paper page
- U Schroeders, 2016, Do the smart get smarter? Development of fluid and crystallized intelligence in 3rd grade, Intelligence, 59, 84, 10.1016/j.intell.2016.08.003 · DOI
- ET Cokely, Cambridge Handbook of Expertise and Expert Performance
- A Vishwanath, 2016, Suspicion, Cognition, and Automaticity Model of Phishing Susceptibility, Communication Research
- K Parsons, 2015, The design of phishing studies: Challenges for researchers, Computers & Security, 52, 194, 10.1016/j.cose.2015.02.008 · DOI
- S Sheng, 2007, SOUPS '07 Proceedings of the 3rd symposium on Usable privacy and security, 88
- J Wang, 2016, Overconfidence in phishing email detection, Journal of the Association of Information Systems, 17, 759, 10.17705/1jais.00442 · DOI
- LM Larson, 2002, Meta-analyses of Big Six Interests and Big Five Personality Factors, Journal of Vocational Behavior, 61, 217, 10.1006/jvbe.2001.1854 · DOI
- T Halevi, 2013, WWW '13 Companion Proceedings of the 22nd International Conference on World Wide Web, 737
- S Sheng, 2010, Who falls for phish? CHI '10 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 373
- RT Wright, 2010, The influence of experiential and dispositional factors in phishing: An empirical investigation of the deceived, Journal of Management Information Systems, 27, 273, 10.2753/MIS0742-1222270111 · DOI
- H-S Rhee, 2009, Self-efficacy in information security: Its influence on end users' information security practice behavior, Computers & Security, 28, 816, 10.1016/j.cose.2009.05.008 · DOI
- OA Zielinska, 2016, Exploring Expert and Novice Mental Models of Phishing, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59, 1132, 10.1177/1541931215591165 · DOI
- M Blythe, 2011, CHI '11 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 3469
- L Borghans, 2009, Gender Differences in Risk Aversion and Ambiguity Aversion, Journal of the European Economic Association, 7, 649, 10.1162/JEEA.2009.7.2-3.649 · DOI
- Chandrasekaran M, Narayanan M, Upadhyaya S. Phishing Email Detection Based on Structural Properties. Proceedings of 9th Annual NYS Cyber Security Conference; Albany2006.
- Han Y, Shen Y. Accurate spear phishing campaign attribution and early detection. SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing; Pisa, Italy: ACM; 2016. p. 2079–86. · DOI
- I Bose, 2007, Unveiling the mask of phishing: Threats, preventive measures, and responsibilities, Communications of the Association for Information Systems, 19, 24
- ML Jensen, 2017, Training to mitigate phishing attacks using mindfulness techniques, Journal of Management Information Systems, 34, 597, 10.1080/07421222.2017.1334499 · DOI
- B Parmar, 2012, Protecting against spear-phishing, Computer Fraud & Security, 2012, 8, 10.1016/S1361-3723(12)70007-6 · DOI
- B Harrison, 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS) Koloa, 5628
- L Stankov, 1997, The Gf/Gcquickie test battery, Unpublished test battery from the School of Psychology
- Y Jiang, 2015, Metacognition and motivation: Links between confidence, self-protection and self-enhancement, Learning and Individual Differences, 37, 222, 10.1016/j.lindif.2014.11.025 · DOI
- S Kleitman, 2007, Self-confidence and metacognitive processes, Learning and Individual Differences, 17, 161, 10.1016/j.lindif.2007.03.004 · DOI
- G Saucier, 2009, What Are the Most Important Dimensions of Personality? Evidence from Studies of Descriptors in Diverse Languages, Social and Personality Psychology Compass, 3, 620, 10.1111/j.1751-9004.2009.00188.x · DOI
- CM Kelley, 2016, Something Smells Phishy: Exploring Definitions, Consequences, and Reactions to Phishing, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56, 2108, 10.1177/1071181312561447 · DOI
- Nyeste PG, Mayhorn CB. Perceptions of cybersecurity: An exploratory analysis. Proceedings of the 17th World Congress of the International Ergonomics Association Beijing, China.2009.
- K Sijtsma, 2009, On the use, the misuse, and the very limited usefulness of Cronbach’s alpha, Psychometrika, 74, 107, 10.1007/s11336-008-9101-0 · DOI · Paper page
- P Kumaraguru, 2010, Teaching Johnny not to fall for phish, ACM Transactions on Internet Technology (TOIT), 10, 7, 10.1145/1754393.1754396 · DOI
- Canfield, C., Davis, A., Fischhoff, B., Forget, A., Pearman, S., & Thomas, J. (2017, July). Replication: Challenges in using data logs to validate phishing detection ability metrics. In Symposium on Usable Privacy and Security (SOUPS) (Vol. 12).
- R Wash, 2018, In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 492
- Siadati H, Palka S, Siegel A, McCoy D. Measuring the Effectiveness of Embedded Phishing Exercises. In 10th {USENIX} Workshop on Cyber Security Experimentation and Test ({CSET} 17). USENIX} Association}, 2017.
- RM Mohammad, 2015, Tutorial and critical analysis of phishing websites methods, Computer Science Review, 17, 1, 10.1016/j.cosrev.2015.04.001 · DOI
Metadata
- Authors
- Kleitman, S., Law, M. K. H., & Kay, J.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Kleitman, Sabina
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-10-26
- Volume
- 13
- Issue
- 10
- Pages
- e0205089
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6203253
- OpenAlex
- W2898538513
- Cited by
- 51
- References
- 1
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 2 · 2023: 7 · 2022: 6 · 2021: 9 · 2020: 6 · 2019: 6
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Sydney
- Faculty of Engineering and Information Technology, University of Technology Sydney
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- phishing susceptibility, phishing detection, false positives, intelligence, Big Five personality traits, individual differences
- MeSH terms
- Adolescent, Adult, Female, Humans, Individuality, Intelligence, Judgment, Deception, Male, Recognition, Psychology, Electronic Mail, Trust, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review