Title
Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions
Abstract
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.
Year
DOI
Venue
2019
10.23919/CNSM46954.2019.9012668
2019 15th International Conference on Network and Service Management (CNSM)
Keywords
DocType
ISSN
Distributed anomaly detection,Bayesian Network,Named Function Networking
Conference
2165-9605
ISBN
Citations 
PageRank 
978-1-7281-5396-4
0
0.34
References 
Authors
15
5
Name
Order
Citations
PageRank
Hoang Long Mai182.47
Guillaume Doyen29813.25
Wissam Mallouli313019.86
Edgardo Montes de Oca49312.06
Olivier Festor566585.40