Detection of malicious websites using machine learning approach and web vulnerability scanner-Reference-Cited by-同舟云学术

Detection of malicious websites using machine learning approach and web vulnerability scanner

Published:2023 Issue: Volume: Page:
ISSN:0094-243X
Container-title:PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY
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Author:

Anjaneyulu M.,Madhuravani B.,Devika P.

Publisher

AIP Publishing

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0114391

Reference8 articles.

1. Tao, Wang, Yu Shunzheng, and Xie Bailin. “A novel framework for learning to detect malicious web pages.” In 2010 InternationalForumonInformationTechnologyandApplications,vol. 2, pp. 353–357. Ieee, 2010.

2. Eshete, Birhanu, Adolfo Villafiorita, and Komminist Weldemariam. “Malicious website detection: Effectiveness and efficiency issues.” In 2011FirstSysSecWorkshop, pp. 123–126. IEEE, 2011.

3. Peneti, S., Sunil Kumar, M., Kallam, S. et al. BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications. Wireless Per Communication (2021).

4. Sachee Nene and R. Priya, “Prediction of Crop yield using Machine Learning”, International Research Journal of Engineering and Technology (IRlET), vol. 05, no. 02, Feb 2018.

5. Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine

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