Open AccessRBFNN: a radial basis function neural network model for detecting and mitigating the cache pollution attacks in named data networking
Author(s) -
Buvanesvari Ramachandira Moorthy,
Suresh Joseph Kanagaraj
Publication year - 2020
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2019.0156
Subject(s) - cache , computer science , locality , computer network , the internet , cache pollution , cache algorithms , locality of reference , computer security , distributed computing , cpu cache , operating system , philosophy , linguistics
Named data networking (NDN) corresponds to content‐centric networking, content‐based networking, and data‐oriented networking. Future internet architecture is modelled to overcome the fundamental limitations of the present internet protocol‐based internet and to provide specific strong security. Caching is a key NDN feature in the network. However, pervasive caching strengthens security issues in particular cache pollution assaults including cache poisoning (e.g. presenting malicious content in caches as false‐locality) and cache pollution (e.g. unpopular content is ruined with cache locality as locality‐disruption). In this work, a new cache replacement method based on the radial basis function neural network (RBFNN) is proposed to detect and mitigate the cache pollution attacks in NDN. RBFNN framework is constructed utilising the input associated with the cached content inherent characteristics and output data associated with the content type, i.e. locality‐disruption, false‐locality, and healthy. Experimental results show the efficiency as well as the effectiveness of the proposed method in terms of hit damage ratio and computational time.