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Phishing Detection: A Literature Survey
Abstract
This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.
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Cited by
A comprehensive survey of AI-enabled phishing attacks detection techniques
Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021) · Telecommunication Systems · doi:10.1007/s11235-020-00733-2 · Cited by 429
Botnet in DDoS Attacks: Trends and Challenges
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2457491 · Cited by 299
Machine learning in cybersecurity: a comprehensive survey
Dasgupta, D., Akhtar, Z., & Sen, S. (2022) · The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology · doi:10.1177/1548512920951275 · Cited by 259
Analysis and prevention of AI-based phishing email attacks
Eze, C. S., & Shamir, L. (2024) · Electronics · doi:10.3390/electronics13101839 · Cited by 47
APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn users
Desolda, G., Greco, F., & Vigano, L. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3733049 · Cited by 16
Metadata
- Authors
- Khonji, M., Iraqi, Y., & Jones, A.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2013
- Volume
- 15
- Issue
- 4
- Pages
- 2091-2121
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2028223155
- Cited by
- 605
- References
- 0
- FWCI
- 51.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.