Biblio

Content-centric networking is emerging as a credible alternative to host-centric networking, especially in scenarios of large-scale content distribution and where privacy requirements are crucial. Recently, research on content-centric networking has focused on security aspects and proposed solutions aimed to protect the network from attacks targeting the content delivery protocols. Content-centric networks are based on the strong assumption of being able to access genuine content from genuine nodes, which is however unrealistic and could open the door to disruptive attacks. Network node misbehavior, either due to poisoning attacks or malfunctioning, can act as a persistent threat that goes unnoticed and causes dangerous consequences. In this paper, we propose a novel certification methodology for content-centric networks that improves transparency and increases trustworthiness of the network and its nodes. The proposed approach builds on behavioral analysis and implements a continuous certification process that collects evidence from the network nodes and verifies their non-functional properties using a rule-based inference model. Utility, performance, and soundness of our approach have been experimentally evaluated on a simulated Named Data Networking (NDN) network targeting properties availability, integrity, and non-repudiation.