AlgoXSSF: Detection and Analysis of Cross-Site Request Forgery (XSRF) and Cross-Site Scripting (XSS) Attacks via Machine Learning Algorithms-Reference-Cited by-同舟云学术

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AlgoXSSF: Detection and Analysis of Cross-Site Request Forgery (XSRF) and Cross-Site Scripting (XSS) Attacks via Machine Learning Algorithms

Published:2024-04-29 Issue: Volume:39 Page:1-8
ISSN:
Container-title:2024 12th International Symposium on Digital Forensics and Security (ISDFS)
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Author:

Kshetri Naresh¹,Kumar Dilip²,Hutson James³,Kaur Navneet⁴,Osama Omar Faruq⁵

Affiliation:

1. School of Business & Technology, Emporia State University,Emporia,KS,USA

2. Laboratory of Applied Cryptology and Security (LACS), Indian Institute of Technology,Mandi,India

3. Lindenwood University,Department of Art History & Culture,Saint Charles,Missouri

4. University of Missouri,Department of Computer Science,St. Louis,Missouri,USA

5. Binghamton University, SUNY,Department of Systems Science and Industrial Engineering,Binghamton,NY,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10526462/10526466/10527278.pdf?arnumber=10527278

Reference51 articles.

1. Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey

2. Impact of Cybercrime on individuals, businesses, and society;Khakimov;Best Journal of Innov in Science, Res & Dev,2023

3. Recent Advancements in Machine Learning for Cybercrime Prediction

4. Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics

5. Log4j Vulnerability

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