An Intelligent ICMPv6 DDoS Flooding-Attack Detection Framework (v6IIDS) using Back-Propagation Neural Network

IPv6 was designed to solve the issue of adopting IPv4 addresses by presenting a large number of address spaces. Currently, many networking devices consider IPv6 as a supportive IPv6-enabled device that includes routers, notebooks, personal computers, and mobile phones. Security has increasingly become a significant issue in exploiting networks and obtaining the benefits of IPv6. One of the important protocols in IPv6 implementation that is used for neighbor and router discovery is ICMPv6. However, this protocol can be used by attackers to deny network services through ICMPv6 DDoS flooding attacks that decrease the network performance. To solve this problem, this study proposes an intelligent ICMPv6 DDoS flooding-attack detection framework using back-propagation neural network (v6IIDS) in IPv6 networks. This study also explores and analyzes the detection accuracy of the proposed v6IIDS framework. The effectiveness of the v6IIDS framework is demonstrated by using real data-sets obtained from an NAv6 laboratory. The data-set traffic is based on a test-bed environment created on the basis of certain parameters used as inputs to generate a new data-set. The results prove that the proposed framework is capable of detecting ICMPv6 DDoS flood attacks with a detection accuracy rate of 98.3%.