A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Article


Karami, A. and Guerrero-Zapata, Manel 2014. A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking. Neurocomputing. 151 (3), pp. 1262-1282.
AuthorsKarami, A. and Guerrero-Zapata, Manel
Abstract

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

KeywordsNamed Data Networking; DoS attacks; Intelligent hybrid algorithm; RBF neural networks; Particle Swarm Optimization; NSGA II
JournalNeurocomputing
Journal citation151 (3), pp. 1262-1282
ISSN0925-2312
Year2014
PublisherElsevier
Accepted author manuscript
License
CC BY-NC-ND
Digital Object Identifier (DOI)doi:10.1016/j.neucom.2014.11.003
Publication dates
Print11 Nov 2014
Publication process dates
Deposited14 Feb 2017
Accepted01 Nov 2014
Copyright information© 2014 Elsevier
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