Reducing Errors in the Anomaly-based Detection of Web-based Attacks Through the Combined Analysis of Web Requests and SQL Queries

Giovanni Vigna, Fredrik Valeur, Davide Balzarotti, William Robertson, Christopher Kruegel
In Journal of Computer Security 17 (3)

anomaly detection intrusion detection machine learning web security

Web-based applications have become a popular means of exposing functionality to large numbers of users by leveraging the services provided by web servers and databases. The wide proliferation of custom-developed web-based applications suggests that anomaly detection could be a suitable approach for providing early warning and real-time blocking of application-level exploits. Therefore, a number of research prototypes and commercial products that learn the normal usage patterns of web applications have been developed. Anomaly detection techniques, however, are prone to both false positives and false negatives. As a result, if anomalous web requests are simply blocked, it is likely that some legitimate requests would be denied, resulting in decreased availability. On the other hand, if malicious requests are allowed to access a web application’s data stored in a back-end database, security-critical information could be leaked to an attacker.

To ameliorate this situation, we propose a system composed of a web-based anomaly detection system, a reverse HTTP proxy, and a database anomaly detection system. Serially composing a web-based anomaly detector and a SQL query anomaly detector increases the detection rate of our system. To address a potential increase in the false positive rate, we leverage an anomaly-driven reverse HTTP proxy to serve anomalous-but-benign requests that do not require access to sensitive information. We developed a prototype of our approach and evaluated its applicability with respect to several existing web-based applications, showing that our approach is both feasible and effective in reducing both false positives and false negatives.