Using Reinforcement Learning for Security Testing: A Systematic Mapping Study

Security of software systems has become increasingly important due to the advancement in technology that occurs on a daily basis and due to the interconnectivity that the Internet network system provides. The manual testing process is time-consuming process and inefficient, especially for very large and complex systems. Reinforcement learning has shown promising results in different test generation approaches due to its ability to optimize the test generation process towards relevant parts of the system. A considerable body of work has been developed in recent years to exploit reinforcement learning for security test generation. This study provides a list of approaches and tools for security test generation using Reinforcement Learning (RL). By searching popular research publication databases, a list of 47 relevant studies has been identified and classified according to the type of approach, RL algorithm, application domain and publication metadata.