skip to main content
10.1145/3526064.3534107acmconferencesArticle/Chapter ViewAbstractPublication PageshpdcConference Proceedingsconference-collections
research-article
Open Access

Predicting Phishing Victimization: Roles of Protective and Vulnerable Strategies and Decision-Making Styles

Published:27 June 2022Publication History

ABSTRACT

Phishing is a common vector for cybercrime and hacking. This research examines participants' personality styles (e.g. decision-making styles, self-control) and the likelihood of falling victim to phishing attacks. Over 300 participants completed an online survey assessing protective and vulnerable strategies, personality styles, trust in people, prior victimization from catphishing or identity theft, and demographics information. Unbeknownst to the participants, 2 to 4 weeks after completing the survey they received a phishing e-mail asking them to click on a link. Individuals with a stronger systematic decision-making style were more likely to have a greater number of protective strategies, and those with greater protective strategies were less likely to be a victim of catphishing and identity theft. Individuals with low avoidant decision-making styles and prior vulnerable strategies were more likely to be phished. These findings suggest that learning protective strategies and not using vulnerable strategies are insufficient to lower substantially the risk of being phished. Training might be improved through considering the match between decision-making styles and the content of the training.

References

  1. A. Alnajim and M. Munro. 2008. An evaluation of users' tips effectiveness for Phishing websites detection. In 2008 Third International Conference on Digital Information Management. 63--68. https://doi.org/10.1109/ICDIM.2008.4746717Google ScholarGoogle Scholar
  2. Z. Alqarni, A. Algarni, and Y. Xu. 2016. Toward Predicting Susceptibility to Phishing Victimization on Facebook. In 2016 IEEE International Conference on Services Computing (SCC). 419--426. https://doi.org/10.1109/SCC.2016.61Google ScholarGoogle ScholarCross RefCross Ref
  3. A. J. Burns, M. Johnson, and D. Caputo. 2019. Spear phishing in a barrel: Insights from a targeted phishing campaign. Journal of Organizational Computing and Electronic Commerce, Vol. 29, 1 (2019), 24--39.Google ScholarGoogle ScholarCross RefCross Ref
  4. C. Dewberry, M. Juanchich, and S. Narendran. 2013a. Decision-making competence in everyday life: The roles of general cognitive styles, decision-making styles and personality. Personality and Individual Differences, Vol. 55, 7 (2013), 783--788.Google ScholarGoogle ScholarCross RefCross Ref
  5. C. Dewberry, M. Juanchich, and S. Narendran. 2013b. The latent structure of decision styles. Personality and Individual Differences, Vol. 54, 5 (2013), 566--571.Google ScholarGoogle ScholarCross RefCross Ref
  6. J. S. Downs, B. Donato, and A. Alessandro. 2015. Predictors of risky decisions: Improving judgment and decision making based on evidence from phishing attacks. Neuroeconomics, judgment, and decision making (2015), 239--253.Google ScholarGoogle Scholar
  7. Serge Egelman, Lorrie Faith Cranor, and Jason Hong. 2008. You've Been Warned: An Empirical Study of the Effectiveness of Web Browser Phishing Warnings. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Florence, Italy) (CHI '08). ACM, New York, NY, USA, 1065--1074. https://doi.org/10.1145/1357054.1357219Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Roderick Graham and Ruth Triplett. 2017. Capable Guardians in the Digital Environment: The Role of Digital Literacy in Reducing Phishing Victimization. Deviant Behavior, Vol. 38, 12 (2017), 1371--1382.Google ScholarGoogle ScholarCross RefCross Ref
  9. Harold G Grasmick, Charles R Tittle, Robert J Bursik Jr, and Bruce J Arneklev. 1993. Testing the core empirical implications of Gottfredson and Hirschi's general theory of crime. Journal of research in crime and delinquency, Vol. 30, 1 (1993), 5--29.Google ScholarGoogle ScholarCross RefCross Ref
  10. Katherine Hamilton, Shin-I Shih, and Susan Mohammed. 2016. The development and validation of the rational and intuitive decision styles scale. Journal of personality assessment, Vol. 98, 5 (2016), 523--535.Google ScholarGoogle ScholarCross RefCross Ref
  11. B. Harrison, A. Vishwanath, Y. J. Ng, and R. Rao. 2015. Examining the Impact of Presence on Individual Phishing Victimization. In 2015 48th Hawaii International Conference on System Sciences. 3483--3489.Google ScholarGoogle Scholar
  12. FBI IC3. 2021. https://pdf.ic3.gov/2021_IC3Report.pdf.Google ScholarGoogle Scholar
  13. J. Jansen and R. Leukfeldt. 2016. Phishing and malware attacks on online banking customers in the Netherlands: A qualitative analysis of factors leading to victimization. International Journal of Cyber Criminology, Vol. 10, 1 (2016), 79.Google ScholarGoogle Scholar
  14. Ponnurangam Kumaraguru, Justin Cranshaw, Alessandro Acquisti, Lorrie Cranor, Jason Hong, Mary Ann Blair, and Theodore Pham. 2009. School of Phish: A Real-World Evaluation of Anti-Phishing Training. In Proceedings of the 5th Symposium on Usable Privacy and Security (SOUPS '09). ACM, New York, NY, USA, Article 3, 12 pages. https://doi.org/10.1145/1572532.1572536Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ponnurangam Kumaraguru, Yong Rhee, Alessandro Acquisti, Lorrie Faith Cranor, Jason Hong, and Elizabeth Nunge. 2007. Protecting People from Phishing: The Design and Evaluation of an Embedded Training Email System. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '07). ACM, New York, NY, USA, 905--914. https://doi.org/10.1145/1240624.1240760Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. Kumaraguru, S. Sheng, A. Acquisti, L. F. Cranor, and J. Hong. 2008. Lessons from a real world evaluation of anti-phishing training. In 2008 eCrime Researchers Summit. 1--12. https://doi.org/10.1109/ECRIME.2008.4696970Google ScholarGoogle Scholar
  17. E. R. Leukfeldt. 2014. Phishing for Suitable Targets in The Netherlands: Routine Activity Theory and Phishing Victimization. Cyberpsychology, Behavior, and Social Networking, Vol. 17, 8 (2014), 551--555. https://doi.org/10.1089/cyber.2014.0008Google ScholarGoogle ScholarCross RefCross Ref
  18. Moez Limayem and Christy MK Cheung. 2011. Predicting the continued use of Internet-based learning technologies: the role of habit. Behaviour & Information Technology, Vol. 30, 1 (2011), 91--99.Google ScholarGoogle ScholarCross RefCross Ref
  19. Eric R Louderback and Olena Antonaccio. 2017. Exploring cognitive decision-making processes, computer-focused cyber deviance involvement and victimization: The role of thoughtfully reflective decision-making. Journal of research in crime and delinquency, Vol. 54, 5 (2017), 639--679.Google ScholarGoogle ScholarCross RefCross Ref
  20. Xin (Robert) Luo, Wei Zhang, Stephen Burd, and Alessandro Seazzu. 2013. Investigating phishing victimization with the Heuristic--Systematic Model: A theoretical framework and an exploration. Computers & Security, Vol. 38 (2013), 28--38. https://doi.org/10.1016/j.cose.2012.12.003 Cybercrime in the Digital Economy.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kalana Malimage. 2013. The role of habit in information security behaviors. Mississippi State University.Google ScholarGoogle Scholar
  22. P. Miksza. 2007. Effective practice: An investigation of observed practice behaviors, self-reported practice habits, and the performance achievement of high school wind players. Journal of Research in Music Education, Vol. 55, 4 (2007), 359--375.Google ScholarGoogle ScholarCross RefCross Ref
  23. Seung Yeop Paek and Mahesh K. Nalla. 2015. The relationship between receiving phishing attempt and identity theft victimization in South Korea. International Journal of Law, Crime and Justice, Vol. 43, 4 (2015), 626--642. https://doi.org/10.1016/j.ijlcj.2015.02.003Google ScholarGoogle ScholarCross RefCross Ref
  24. Wendy J Phillips, Jennifer M Fletcher, Anthony DG Marks, and Donald W Hine. 2016. Thinking styles and decision making: A meta-analysis. Psychological Bulletin, Vol. 142, 3 (2016), 260.Google ScholarGoogle ScholarCross RefCross Ref
  25. A. Piquero and A. Rosay. 1998. The reliability and validity of Grasmick et al.'s self-control scale: A comment on Longshore et al. Criminology, Vol. 36 (1998), 157.Google ScholarGoogle ScholarCross RefCross Ref
  26. Travis C Pratt, Jillian J Turanovic, Kathleen A Fox, and Kevin A Wright. 2014. Self-control and victimization: A meta-analysis. Criminology, Vol. 52, 1 (2014), 87--116.Google ScholarGoogle ScholarCross RefCross Ref
  27. Stephanie A Prince, Luca Cardilli, Jennifer L Reed, Travis J Saunders, Chris Kite, Kevin Douillette, Karine Fournier, and John P Buckley. 2020. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, Vol. 17, 1 (2020), 1--17.Google ScholarGoogle ScholarCross RefCross Ref
  28. Swapan Purkait. 2012. Phishing counter measures and their effectiveness--literature review. Information Management & Computer Security (2012).Google ScholarGoogle Scholar
  29. Susanne G Scott and Reginald A Bruce. 1995. Decision-making style: The development and assessment of a new measure. Educational and psychological measurement, Vol. 55, 5 (1995), 818--831.Google ScholarGoogle Scholar
  30. Steve Sheng, Mandy Holbrook, Ponnurangam Kumaraguru, Lorrie Faith Cranor, and Julie Downs. 2010. Who Falls for Phish? A Demographic Analysis of Phishing Susceptibility and Effectiveness of Interventions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 373--382. https://doi.org/10.1145/1753326.1753383Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Hossein Siadati, S. Palka, A. Siegel, and D. McCoy. 2017. Measuring the Effectiveness of Embedded Phishing Exercises. In 10th Workshop on Cyber Security Experimentation and Test (CSET 17). USENIX Association, Vancouver, BC.Google ScholarGoogle Scholar
  32. Emma Soane, Iljana Schubert, Rebecca Lunn, and Simon Pollard. 2015. The relationship between information processing style and information seeking, and its moderation by affect and perceived usefulness: Analysis vs. procrastination. Personality and Individual Differences, Vol. 72 (2015), 72--78.Google ScholarGoogle ScholarCross RefCross Ref
  33. Bas Verplanken and Henk Aarts. 1999. Habit, Attitude, and Planned Behaviour: Is Habit an Empty Construct or an Interesting Case of Goal-directed Automaticity? European Review of Social Psychology, Vol. 10, 1 (1999), 101--134. https://doi.org/10.1080/14792779943000035Google ScholarGoogle ScholarCross RefCross Ref
  34. A. Vishwanath. 2015. Habitual Facebook use and its impact on getting deceived on social media. Journal of Computer-Mediated Communication, Vol. 20, 1 (2015), 83--98.Google ScholarGoogle ScholarCross RefCross Ref
  35. Arun Vishwanath. 2016. Mobile device affordance: Explicating how smartphones influence the outcome of phishing attacks. Computers in Human Behavior, Vol. 63 (2016), 198--207.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Arun Vishwanath, Brynne Harrison, and Yu Jie Ng. 2018. Suspicion, cognition, and automaticity model of phishing susceptibility. Communication Research, Vol. 45, 8 (2018), 1146--1166.Google ScholarGoogle ScholarCross RefCross Ref
  37. Arun Vishwanath, Tejaswini Herath, Rui Chen, Jingguo Wang, and H Raghav Rao. 2011. Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model. Decision Support Systems, Vol. 51, 3 (2011), 576--586.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Predicting Phishing Victimization: Roles of Protective and Vulnerable Strategies and Decision-Making Styles

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SNTA '22: Fifth International Workshop on Systems and Network Telemetry and Analytics
        June 2022
        62 pages
        ISBN:9781450393157
        DOI:10.1145/3526064

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 June 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate22of106submissions,21%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader