SQL Injection Detection with Composite Kernel in Support Vector Machine
Modern web application systems are generally consisted of database systems in order to process and store business information. These systems are highly interesting to hackers as they contain sensitive information and the diversity and amount of attacks severely undermine the effectiveness of classical signature-based detection. In this paper, the authors propose a novel approach for learning SQL statements and apply machine learning techniques, such as one class classification, in order to detect malicious behavior between the database and application. The approach incorporates the tree structure of SQL queries as well as input parameter and query value similarity as characteristic to distinguish malicious from benign queries. They develop the learning system integrated in PHP and demonstrate the usefulness of their approach on real-world application.