Enhancing web application attack detection using machine learning
Resumen:
The exploit of vulnerabilities present in Web applications has been the main attack vector in the last decade biggest data breaches. In this work we put forward a framework to leverage the performance of Web Application Firewalls (WAFs) using machine learning techniques. We propose the use of two types of machine learning models: a multi-class approach for the scenario when valid and attack data is available and alternatively a one-class model when only valid data is at hand. The use of both models to predict potential malicious traffic has shown to outperform MODSECURITY, a widely deployed WAF technology, configured with the OWASP Core Rule Set out of the box. We also present a prototype that integrates the one-class model with MODSECURITY.
| 2018 | |
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Web Application Firewall Web Application Security Machine Learning Pattern Recognition |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/29285 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
