4.6 Article

Combining Popularity and Locality to Enhance In-Network Caching Performance and Mitigate Pollution Attacks in Content-Centric Networking

Journal

IEEE ACCESS
Volume 5, Issue -, Pages 19012-19022

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2754058

Keywords

Content-centric networking; caching allocation; pollution attacks; locality; popularity

Funding

  1. NSF of China [61672092, 61363081, 61572066]

Ask authors/readers for more resources

Content-centric networking (CCN) aims to improve network reliability, scalability, and security by changing the way that information is organized and retrieved in the current Internet. One critical issue in CCN is in-network cache allocation. It is known that in-network caching mechanisms are vulnerable to distributed denial of service attacks, especially to pollution attacks. That is, a caching mechanism under pollution attacks cannot work well. The past years witnessed kinds of proposals of cache allocation mechanisms. However, none of them could effectively allocate in-network cache while defending against attacks. In this paper, we propose a lightweight non-collaborative cache allocation approach (IRDD), which could not only enhance in-network caching performance in terms of the cache hit ratio and the request processing delay, but also defend against pollution attacks. By lightweight, we mean that IRDD generates low communication overhead (due to non-collaboration) and computational overhead at routers. The key idea behind IRDD is to combine the content popularity with the content locality in making caching decision. Extensive simulation results on ndnSIM platform demonstrate the capability of the proposed approach in improving cache allocation performance while reducing the impact of pollution attacks.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available