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

• Published in: Neurocomputing (Amsterdam) Vol. 151; pp. 1262 - 1282
• Main Authors: Karami, Amin, Guerrero-Zapata, Manel
• Format: Journal Article Publication
• Language: English
• Published: Elsevier B.V 03.03.2015
• Subjects: Adaptive algorithms; Algorismes genètics; Algorithms; Arquitectura de computadors; Computer networks; Computer simulation; Denial of service attacks; DoS attacks; Genetic algorithms; Hybrid systems; Informàtica; Intelligent hybrid algorithm; Internet; Mesures de seguretat; Named Data Networking; Networks; Neural networks; NSGA II; Ordinadors, Xarxes d; Particle Swarm Optimization; RBF neural networks; Security measures; Àrees temàtiques de la UPC; DoS attacks; RBF neural networks; Intelligent hybrid algorithm; Particle Swarm Optimization; Named Data Networking; NSGA II
• ISSN: 0925-2312; 1872-8286; 1872-8286
• DOI: 10.1016/j.neucom.2014.11.003

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.