Collaborative remediation of configuration vulnerabilities in autonomic networks and systems

Autonomic computing has become an important paradigm for dealing with large scale network management. However, changes operated by administrators and self-governed entities may generate vulnerable configurations increasing the exposure to security attacks. In this paper, we propose a novel approach for supporting collaborative treatments in order to remediate known security vulnerabilities in autonomic networks and systems. We put forward a mathematical formulation of vulnerability treatments as well as an XCCDF-based language for specifying them in a machine-readable manner. We describe a collaborative framework for performing these treatments taking advantage of optimized algorithms, and evaluate its performance in order to show the feasibility of our solution.