A Comparative Study of Machine Learning Techniques for Phishing Website Detection-Reference-Cited by-同舟云学术

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A Comparative Study of Machine Learning Techniques for Phishing Website Detection

Published:2023 Issue: Volume: Page:97-109
ISSN:2522-8595
Container-title:Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions
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Author:

Khan Mohammad Farhan,Tiwari Rohit Kumar,Saroj Sushil Kumar,Tripathi Tripti

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-15542-0_6

Reference13 articles.

1. Singh, P., Maravi, Y. P., Sharma, S. (2015, February). Phishing websites detection through supervised learning networks. In 2015 international conference on computing and communications technologies (ICCCT) (pp. 61–65). IEEE.

2. HeidiBleau. RSA quarterly fraud report: Q4 2018. URL: https://www.rsa.com/en-us/offers/rsa-fraud-report-q4-2018. Accessed 15 Sept 2021.

3. Rao, R. S., & Pais, A. R. (2019). Detection of phishing websites using an efficient feature-based machine learning framework. Neural Computing and Applications, 31(8), 3851–3873.

4. Sönmez, Y., Tuncer, T., Gökal, H., & Avcı, E. (2018, March). Phishing web sites features classification based on extreme learning machine. In 2018 6th international symposium on digital forensic and security (ISDFS) (pp. 1–5). IEEE.

5. Sharmin, S., & Zaman, Z. (2017, December). Spam detection in social media employing machine learning tool for text mining. In 2017 13th international conference on signal-image Technology & Internet-Based Systems (SITIS) (pp. 137–142). IEEE.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Data-Driven Approach for Online Phishing Activity Detection;2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS);2024-03-08

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