A Comparison of Performance of Rough Set Theory with Machine Learning Techniques in Detecting Phishing Attack
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-87049-2_22
Reference48 articles.
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3. Alloghani, M., Al-Jumeily, D., Hussain, A., Mustafina, J., Baker, T., ljaaf, A.J.: Implementation of machine learning and data mining to improve cybersecurity and limit vulnerabilities to cyber attacks. In: Nature-Inspired Computation in Data Mining and Machine Learning, pp. 47–76. Springer, Cham (2020)
4. APWG. Accessed on 2 May 2020. https://docs.apwg.org/reports/apwg_trends_report_q3_2019.pdf
5. Aydin, M., Butun, I., Bicakci, K., Baykal, N.: Using attribute-based feature selection approaches and machine learning algorithms for detecting fraudulent website URLs. In: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0774–0779. IEEE (2020)
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