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Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery
Authors
1Assistant professor, Department of Information Technology, CMR Engineering College (UGC Autonomous), Hyderabad, Telangana, India. 2,3,4,5 B.Tech IV-Year, Department of Information Technology, CMREC, (UGC Autonomous), Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 4, Issue 1 (2024)
- Published Online
- January-February 2024
- Pages
- 26-29
- Article Type
- Research Article
- Check for updates
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References
1. S. Calzavara, R. Focardi, M. Squarcina and M. Tempesta, "Surviving the web: A journey into web session security", ACM Comput.
Surv., vol. 50, no. 1, pp. 13:1-13:34, 2017.
2. A. Sudhodanan, R. Carbone, L. Compagna, N. Dolgin, A. Armando and U. Morelli, "Large-scale analysis & detection of authentication
cross-site request forgeries", Proc. 2017 IEEE European Symp. Security and Privacy (EuroS&P 2017), pp. 350-365
3. S. Calzavara, A. Rabitti, A. Ragazzo and M. Bugliesi, "Testing for integrity flaws in web sessions", Proc. Computer Security 24rd
European Symp. Research Computer Security (ESORICS 2019), pp. 606-624, Sept. 2019.
4. J. Bau, E. Bursztein, D. Gupta and J. C. Mitchell, "State of the art: Automated black-box web application vulnerability testing", Proc.
31st IEEE Symp. Security and Privacy (S&P 2010), pp. 332-345, May 2010.
5. A. Doupé, M. Cova and G. Vigna, "Why Johnny can’t pentest: An analysis of black-box web vulnerability scanners", Proc. 7th Int. Conf.
Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA 2010), pp. 111-131, July 2010.
6. Barth, C. Jackson and J. C. Mitchell, "Robust defenses for cross-site request forgery", Proc. 2008 ACM Conf. Computer and
Communications Security (CCS 2008), pp. 75-88.
7. M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, Cambridge, MA:MIT Press, 2012
8. . Calzavara, G. Tolomei, A. Casini, M. Bugliesi and S. Orlando, "A supervised learning approach to protect client authentication on the
web", ACM Trans. Web, vol. 9, no. 3, pp. 15:1-15:30, 2015.
9. S. Calzavara, M. Conti, R. Focardi, A. Rabitti and G. Tolomei, "Mitch: A machine learning approach to the black-box detection of
CSRF vulnerabilities", Proc. IEEE European Symp. Security and Privacy (EuroS&P 2019), pp. 528-543, June 2019.
10. G. Pellegrino, M. Johns, S. Koch, M. Backes and C. Rossow, "Deemon: Detecting CSRF with dynamic analysis and property graph",
Proc. 2017 ACM SIGSAC Conf. Computer and Communications Security(CCS 2017), pp. 1757-1771, Oct. 2017.
Surv., vol. 50, no. 1, pp. 13:1-13:34, 2017.
2. A. Sudhodanan, R. Carbone, L. Compagna, N. Dolgin, A. Armando and U. Morelli, "Large-scale analysis & detection of authentication
cross-site request forgeries", Proc. 2017 IEEE European Symp. Security and Privacy (EuroS&P 2017), pp. 350-365
3. S. Calzavara, A. Rabitti, A. Ragazzo and M. Bugliesi, "Testing for integrity flaws in web sessions", Proc. Computer Security 24rd
European Symp. Research Computer Security (ESORICS 2019), pp. 606-624, Sept. 2019.
4. J. Bau, E. Bursztein, D. Gupta and J. C. Mitchell, "State of the art: Automated black-box web application vulnerability testing", Proc.
31st IEEE Symp. Security and Privacy (S&P 2010), pp. 332-345, May 2010.
5. A. Doupé, M. Cova and G. Vigna, "Why Johnny can’t pentest: An analysis of black-box web vulnerability scanners", Proc. 7th Int. Conf.
Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA 2010), pp. 111-131, July 2010.
6. Barth, C. Jackson and J. C. Mitchell, "Robust defenses for cross-site request forgery", Proc. 2008 ACM Conf. Computer and
Communications Security (CCS 2008), pp. 75-88.
7. M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, Cambridge, MA:MIT Press, 2012
8. . Calzavara, G. Tolomei, A. Casini, M. Bugliesi and S. Orlando, "A supervised learning approach to protect client authentication on the
web", ACM Trans. Web, vol. 9, no. 3, pp. 15:1-15:30, 2015.
9. S. Calzavara, M. Conti, R. Focardi, A. Rabitti and G. Tolomei, "Mitch: A machine learning approach to the black-box detection of
CSRF vulnerabilities", Proc. IEEE European Symp. Security and Privacy (EuroS&P 2019), pp. 528-543, June 2019.
10. G. Pellegrino, M. Johns, S. Koch, M. Backes and C. Rossow, "Deemon: Detecting CSRF with dynamic analysis and property graph",
Proc. 2017 ACM SIGSAC Conf. Computer and Communications Security(CCS 2017), pp. 1757-1771, Oct. 2017.
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