The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A survey of phishing attacks: Their types, vectors and technical approaches
Abstract
Abstract Phishing was a threat in the cyber world a couple of decades ago and still is today. It has grown and evolved over the years as phishers are getting creative in planning and executing the attacks. Thus, there is a need for a review of the past and current phishing approaches. A systematic, comprehensive and easy-to-follow review of these approaches is presented here. The relevant mediums and vectors of these approaches are identified for each approach. The medium is the platform which the approaches reside and the vector is the means of propagation utilised by the phisher to deploy the attack. The paper focuses primarily on the detailed discussion of these approaches. The combination of these approaches that the phishers utilised in conducting their phishing attacks is also discussed. This review will give a better understanding of the characteristics of the existing phishing techniques which then acts as a stepping stone to the development of a holistic anti-phishing system. This review creates awareness of these phishing techniques and encourages the practice of phishing prevention among the readers. Furthermore, this review will gear the research direction through the types of phishing, while also allowing the identification of areas where the anti-phishing effort is lacking. This review will benefit not only the developers of anti-phishing techniques but the policy makers as well.
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
Detection and analysis of drive-by-download attacks and malicious JavaScript code
Cova, M., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772720 · Cited by 580
Advanced social engineering attacks
Krombholz, K., Hobel, H., Huber, M., & Weippl, E. (2015) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2014.09.005 · Cited by 575
Decision strategies and susceptibility to phishing
Downs, J. S., Holbrook, M. B., & Cranor, L. F. (2006) · Proceedings of the second symposium on Usable privacy and security - SOUPS '06 · doi:10.1145/1143120.1143131 · Cited by 352
A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
Heartfield, R., & Loukas, G. (2016) · ACM Computing Surveys · doi:10.1145/2835375 · Cited by 202
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
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E., Japkowicz, N., & Viktor, H. (2022) · arXiv · doi:10.48550/arxiv.2210.07321 · Cited by 196
“This might be to convince the target to open an unsafe document that contains an exploit, cause the target to navigate to a fake banking webpage, encourage them to share sensitive information that can be used for identity theft, among many other documented methods [5, 36].”
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196
“This might be to convince the target to open an unsafe document that contains an exploit, cause the target to navigate to a fake banking webpage, encourage them to share sensitive information that can be used for identity theft, among many other documented methods [5, 36].”
A survey of phishing attack techniques, defence mechanisms and open research challenges
Jain, A. K., & Gupta, B. B. (2022) · Enterprise Information Systems · doi:10.1080/17517575.2021.1896786 · Cited by 192
Hannousse, A., & Yahiouche, S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104347 · Cited by 157
Metadata
- Authors
- Chiew, K. L., Yong, K. S. C., & Tan, C. L.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 106
- Pages
- 1-20
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2794598542
- Cited by
- 307
- References
- 4
- FWCI
- 43.79
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A survey of phishing attacks: Their types, vectors and technical approaches. https://app.cochat.ai/papers/10.1016/j.eswa.2018.03.050
- The paper
- Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.03.050
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.