Learning Autonomic Security Reconfiguration Policies

Juan E. Tapiador, John A. Clark
2010 2010 10th IEEE International Conference on Computer and Information Technology  
We explore the idea of applying machine learning techniques to automatically infer risk-adaptive policies to reconfigure a network security architecture when the context in which it operates changes. To illustrate our approach, we consider the case of a MANET where nodes carrying sensitive services (e.g., web servers, key repositories, etc.) should consider relocating themselves into a different node to guarantee proper functioning. We use simulation to derive properties from a candidate
more » ... a candidate policy, and then apply Genetic Programming and Multi-Objective Optimisation techniques to search for optimal candidates. The inferred policies take the form of risk-aware service relocation algorithms that autonomously dictate when and how to relocate services with the aim of keeping risk to a minimum. Since security policies often have implications in dimensions other than security, we force the learning process to consider also the consequences (performance, usability) of a given policy.
doi:10.1109/cit.2010.168 dblp:conf/IEEEcit/TapiadorC10 fatcat:wgbqm3bdz5h43gmkl5cfmezr7u