Enhancing web application attack detection using machine learning

 

Autor(es):
Martínez, Rodrigo
Tipo:
Preprint
Versión:
Enviado
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.

Año:
2018
Idioma:
Inglés
Temas:
Web Application Firewall
Web Application Security
Machine Learning
Pattern Recognition
Institución:
Universidad de la República
Repositorio:
COLIBRI
Enlace(s):
https://hdl.handle.net/20.500.12008/29285
Nivel de acceso:
Acceso abierto