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

作者：Karami Amin^*; Guerrero Zapata Mane

来源：Neurocomputing, 2015, 151: 1262-1282.

DOI：10.1016/j.neucom.2014.11.003

• Named Data Networking
• DoS attacks
• Intelligent hybrid algorithm
• RBF neural networks
• Particle Swarm Optimization
• NSGA II

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.

• 出版日期2015-3-3