Generic Approach for Security Error Detection Based on Learned System Behavior Models for Automated Security Tests

The increasing complexity of software and IT systems creates the necessity for research on technologies addressing current key security challenges. To meet security requirements in IT infrastructures, a security engineering process has to be established. One crucial factor contributing to a higher level of security is the reliable detection of security vulnerabilities during security tests. In the presented approach, we observe the behavior of the system under test and introduce machine learning methods based on derived behavior metrics. This is a generic method for different test targets which improves the accuracy of the security test result of an automated security testing approach. Reliable automated determination of security failures in security test results increases the security quality of the tested software and avoids costly manual validation.