Phishing Classification Based on Text Content of an Email Body Using Transformers
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5091-1_25
Reference25 articles.
1. Sharma H, Meenakshi E, Bhatia SK (2017) A comparative analysis and awareness survey of phishing detection tools. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE, pp 1437–1442
2. APWG (2022) APWG 2022 phishing activity trends reports, first quarter 2022. https://docs.apwg.org/reports/apwg_trends_report_q1_2022.pdf. Accessed 07 June 2022
3. Salloum S, Gaber T, Vadera S, Sharan K (2022) A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access
4. Hamid IRA, Abawajy J (2011) Hybrid feature selection for phishing email detection. In: International conference on algorithms and architectures for parallel processing. Springer, pp 266–275
5. Abu-Nimeh S, Nappa D, Wang X, Nair S (2009) Distributed phishing detection by applying variable selection using Bayesian additive regression trees. In: 2009 IEEE international conference on communications. IEEE, pp 1–5
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