Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions

Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.