An optimal statistical test for robust detection against interest flooding attacks in CCN

Confronting the changing demand of users, the current Internet is revealing its limitations. Information Centric Network (ICN) are Future Internet proposals which are based on named data objects. In order to actually replace its predecessor, ICN must be able to resist existent threats in the current Internet, especially the Denial of Service (DoS) attack. In this paper, we focus on Interest flooding - a new type of DoS attack in Content Centric Network (CCN). Several solutions for this threat have been introduced, but they do not solve the problem in a satisfying way because of some drawbacks in either their detection performance, scalability support or restricted scenario of usage. Our goal is to design a reliable, low resources-consuming detection method against Interest flooding attack in CCN. A detection scheme must be attended since a lot of resources consumed by unnecessarily continuous countermeasure can be saved by a dependable detector. Like no other detectors in proposed solutions, our detector is based on statistical hypotheses testing theory. The achieved result is a low resources-consuming detector that can be deployed globally on each CCN router. The false alarm probability of our detector can be controlled at will. Its statistical power can be theoretically established and evaluated precisely. To validate our contribution, numerical results show the relevance of the proposed approach and the sharpness of theoretical results.