SmartiPhish: a reinforcement learning-based intelligent anti-phishing solution to detect spoofed website attacks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Safety, Risk, Reliability and Quality,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10207-023-00778-9.pdf
Reference44 articles.
1. Chiew, K.L., Yong, K.S.C., Tan, C.L.: A survey of phishing attacks: their types, vectors and technical approaches. Expert Syst. Appl. 106, 1–20 (2018). https://doi.org/10.1016/j.eswa.2018.03.050
2. Dou, Z., Khalil, I., Khreishah, A., Al-Fuqaha, A., Guizani, M.: Systematization of knowledge (SoK): a systematic review of software-based web phishing detection. IEEE Commun. Surv. Tutor. 19(4), 2797–2819 (2017). https://doi.org/10.1109/comst.2017.2752087
3. European Union Agency for Cybersecurity: Enisa threat landscape report 2018: 15 top cyber threats and trends. Technical report (2019). https://doi.org/10.2824/622757
4. APWG: Phishing activity trends report: 4th quarter 2021. Technical report, Anti-Phishing Working Group (2022)
5. Huang, H., Zhong, S., Tan, J.: Browser-side countermeasures for deceptive phishing attack. In: 2009 Fifth International Conference on Information Assurance and Security, vol. 1, pp. 352–355 (2009). https://doi.org/10.1109/IAS.2009.12
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