An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking

Abstract

Named Data Networking (NDN) is a candidate next-generation Internet architecture designed to overcome the fundamental limitations of the current IP-based Internet, in particular strong security. The ubiquitous in-network caching is a key NDN feature. However, pervasive caching strengthens security problems namely cache pollution attacks including cache poisoning (i.e., introducing malicious content into caches as false-locality) and cache pollution (i.e., ruining the cache locality with new unpopular content as locality-disruption). In this paper, a new cache replacement method based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to mitigate the cache pollution attacks in NDN. The ANFIS structure is built using the input data related to the inherent characteristics of the cached content and the output related to the content type (i.e., healthy, locality-disruption, and false-locality). The proposed method detects both false-locality and locality-disruption attacks as well as a combination of the two on different topologies with high accuracy, and mitigates them efficiently without very much computational cost as compared to the most common policies.