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Human Cognition Through the Lens of Social Engineering Cyberattacks
Open accessAbstract
Social engineering cyberattacks are a major threat because they often prelude sophisticated and devastating cyberattacks. Social engineering cyberattacks are a kind of psychological attack that exploits weaknesses in human cognitive functions. Adequate defense against social engineering cyberattacks requires a deeper understanding of what aspects of human cognition are exploited by these cyberattacks, why humans are susceptible to these cyberattacks, and how we can minimize or at least mitigate their damage. These questions have received some amount of attention but the state-of-the-art understanding is superficial and scattered in the literature. In this paper, we review human cognition through the lens of social engineering cyberattacks. Then, we propose an extended framework of human cognitive functions to accommodate social engineering cyberattacks. We cast existing studies on various aspects of social engineering cyberattacks into the extended framework, while drawing a number of insights that represent the current understanding and shed light on future research directions. The extended framework might inspire future research endeavor towards a new sub-field that can be called {\em Cybersecurity Cognitive Psychology}, which tailors or adapts principles of Cognitive Psychology to the cybersecurity domain while embracing new notions and concepts that are unique to the cybersecurity domain.
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“A fundamentally important mitigation strategy against social engineering attacks is to strengthen human users through education (Montañez et al., 2020).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Montañez et al., 2020
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Sorted by citations
Influence of Human Factors on Cyber Security within Healthcare Organisations: A Systematic Review
Nifakos, S., Chandramouli, K., Nikolaou, C. K., Papachristou, P., Koch, S., Panaousis, E., & Bonacina, S. (2021) · Sensors · doi:10.3390/s21155119 · Cited by 317
Nicholls, J., Kuppa, A., & Le‐Khac, N. (2021) · IEEE Access · doi:10.1109/access.2021.3134076 · Cited by 221
Bouramdane, A. (2023) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp3040031 · Cited by 127
A Study on the Psychology of Social Engineering-Based Cyberattacks and Existing Countermeasures
Siddiqi, M. A., Pak, W., & Siddiqi, M. A. (2022) · Applied Sciences · doi:10.3390/app12126042 · Cited by 118
The COVID‐19 scamdemic: A survey of phishing attacks and their countermeasures during COVID‐19
Al-Qahtani, A. F., & Cresci, S. (2022) · IET Information Security · doi:10.1049/ise2.12073 · Cited by 68
Metadata
- Authors
- Montañez, R., Golob, E., & Xu, S.
- Affiliations
- The University of Texas at San Antonio (US)
- Corresponding author
- Xu, Shouhuai
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2020-09-30
- Volume
- 11
- Pages
- 1755-1755
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC7554349
- OpenAlex
- W3091679231
- Cited by
- 106
- References
- 119
- Citations by year
- 2026: 21 · 2025: 26 · 2024: 23 · 2023: 17 · 2022: 9 · 2021: 8 · 2020: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1736209, 1814825)
- Army Research Office (W911NF-17, W911NF-17-1-0566, W911NF)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- human cognition, cognitive psychology, cybersecurity, human factors in cybersecurity
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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.
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