The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A comprehensive survey of AI-enabled phishing attacks detection techniques
Open accessAbstract
In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.
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Cites
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102419 · Cited by 1,130
Machine learning based phishing detection from URLs
Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.029 · Cited by 857
Phishing Detection: A Literature Survey
Khonji, M., Iraqi, Y., & Jones, A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.032213.00009 · Cited by 605
Chiew, K. L., Tan, C. L., Wong, K., Yong, K. S. C., & Tiong, W. K. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.064 · Cited by 348
Phishing environments, techniques, and countermeasures: A survey
Aleroud, A., & Zhou, L. (2017) · Computers & Security · doi:10.1016/j.cose.2017.04.006 · Cited by 326
A survey of phishing attacks: Their types, vectors and technical approaches
Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.050 · Cited by 307
Defending against phishing attacks: taxonomy of methods, current issues and future directions
Gupta, B. B., Arachchilage, N. A. G., & Psannis, K. E. (2018) · Telecommunication Systems · doi:10.1007/s11235-017-0334-z · Cited by 293
Fighting against phishing attacks: state of the art and future challenges
Gupta, B. B., Tewari, A., Jain, A. K., & Agrawal, D. P. (2017) · Neural Computing and Applications · doi:10.1007/s00521-016-2275-y · Cited by 289
Javed, A. R., Usman, M., Rehman, S. U., Khan, M. U., & Haghighi, M. S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3025875 · Cited by 223
Self-citationA stacking model using URL and HTML features for phishing webpage detection
Li, Y., Yang, Z., Chen, X., Yuan, H., & Liu, W. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.11.004 · Cited by 222
Cited by
Explainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
“For the specific field of phishing interesting approach is defined in [61] and for ransomware attacks in [62].”
BackgroundCheng, B., Lin, H., & Kong, Y. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113987 · Cited by 272
Digital deception: generative artificial intelligence in social engineering and phishing
Schmitt, M., & Flechais, I. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10973-2 · Cited by 198
Devising and Detecting Phishing Emails Using Large Language Models
Heiding, F., Schneier, B., Vishwanath, A., Bernstein, J., & Park, P. S. (2024) · IEEE Access · doi:10.1109/access.2024.3375882 · Cited by 123
Quantum secured 6G technology-based applications in Internet of Everything
Prateek, K., Ojha, N. K., Altaf, F., & Maity, S. (2023) · Telecommunication Systems · doi:10.1007/s11235-022-00979-y · Cited by 45
Metadata
- Authors
- Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K.
- Venue
- Telecommunication Systems
- Published
- 2020-10-23
- Volume
- 76
- Issue
- 1
- Pages
- 139-154
- ISSN
- 1018-4864 (ISSN-L)
- 1572-9451
- OpenAlex
- W3094117827
- Cited by
- 429
- References
- 35
- FWCI
- 38.12
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems. https://doi.org/10.1007/s11235-020-00733-2
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