Web Vulnerability Detection: The Case of Cross-Site Request Forgery Using Classification and Regression Trees
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2281-7_78
Reference16 articles.
1. Kumar A, Gupta A, Mittal P, Gupta PK, Varghese S (2021) Prevention of XSS attack using Cryptography and API integration with Web Security, 25 April 2021. https://ssrn.com/abstract=3833910, http://dx.doi.org/10.2139/ssrn.3833910
2. Wibowo R, Sulaksono A (2021) Web vulnerability through cross site scripting (XSS) detection with OWASP security Shepherd. Indonesian J Inf Syst 3(2):149–159. https://doi.org/10.24002/ijis.v3i2.4192
3. Arora N, Singh P, Sahu S, Keshari VK, Vinoth Kumar M (2021) Preventing SSRF (Server-Side Request Forgery) and CSRF (Cross-Site Request Forgery) using extended visual cryptography and QR Code. In: Goyal D, Chaturvedi P, Nagar AK, Purohit S (eds) Proceedings of Second International Conference on Smart Energy and Communication. AIS. Springer, Singapore. https://doi.org/10.1007/978-981-15-6707-0_20
4. Hashim A, Medani R, Attia TA (2021) Defences against web application attacks and detecting phishing links using machine learning. In: 2020 international conference on computer, control, electrical, and electronics engineering (ICCCEEE), pp 1–6. https://doi.org/10.1109/ICCCEEE49695.2021.9429609
5. Gogoi B, Ahmed T, Saikia, HK (2021) Detection of XSS attacks in web applications: a machine learning approach. Int J Innov Res Comput Sci Technol (IJIRCST) 9(1):1–10. https://ssrn.com/abstract=3778063. ISSN 2347-5552
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