Supporting vulnerability awareness in autonomic networks and systems with OVAL

Changes that are operated by autonomic networks and systems may generate vulnerabilities and increase the exposure to security attacks. We present in this paper a new approach for increasing vulnerability awareness in such self-managed environments. Our objective is to enable autonomic networks to take advantage of the knowledge provided by vulnerability descriptions in order to maintain safe configurations. In that context, we propose a modeling and an architecture for automatically translating these descriptions into policy rules that are interpretable by an autonomic configuration system. We also describe an implementation prototype and evaluate its performance through an extensive set of experiments.