Neuro-fuzzy-based clustering of DDoS attack detection in the network

Abstract

The detection system developed for wired networks cannot be deployed in wireless networks due to the difference between the two types of networks. The data transmission of the wired network is a standard physical routing. However, the data stream routing of wireless network are based on radio signals with a variety of problems of evolution. The attacker's packet header data are acknowledged with a port number, option field parameters and IP address. The anomalies detection is carried out at a regular interval to monitor the traffic analysed through statistical variance. The change detection detects the statistical variance of the traffic volume. The results obtained from the proposed system are compared with the existing attack detection systems with the propagation delay metric. It shows a reduction of nearly 10% and an improvement of 13% average throughput.