Phishing Detection in Browser-in-the-Middle: A Novel Empirical Approach Incorporating Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Phishing Detection in Browser-in-the-Middle: A Novel Empirical Approach Incorporating Machine Learning Algorithms

Published:2024 Issue: Volume: Page:123-134
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Shahriyar Md. Farhan^ORCID,Seyam Zarif Sadeque,Ullah Ahsan,Sakib Md. Nazmus

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-64067-4_9

Reference24 articles.

1. Lecture Notes in Computer Science;DH Lee,2007

2. 4th Quarter - APWG PHISHING ACTIVITY TRENDS REPORTS. https://docs.apwg.org/reports/apwg_trends_report_q4_2022.pdf. Accessed 18 Sep 2023

3. 2023 Phishing Threats Report. The Cloudflare Blog. cf-as-sets. www.cloudflare.com/slt3lc6tev37/17i1am3UMltoOE7UOU1neX/5776ab933524b3cf99ee85eaaedc76f6/BDES-4838_2023_Phishing-Threats-Report-2023.pdf. Accessed 23 Aug 2023

4. Cost of a data breach 2023. IBM. https://www.ibm.com/reports/data-breach. Accessed 27 Aug 2023

5. Gupta, S., Singhal, A., Kapoor, A.: A literature survey on social engineering attacks: Phishing attack. In: 2016 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, pp. 537–540 (2016). https://doi.org/10.1109/CCAA.2016.7813778

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