Networks security measures using neuro-fuzzy agents

The growing dependence of modern society on telecommunication and information networks and e-type systems has become inevitable. However, those types of systems are vulnerable to malicious attacks. The speed and automation in network attack techniques continue to increase. An achievable automated attack or unauthorised access to a particular organization network
could lead to devastating effects on its reputation and imminent
loss of life. In this paper an innovative way is proposed to detect network attacks of a distributed nature such as denial of service (DoS) attacks. The proposed scheme is mainly based on neuro-fuzzy intelligence in order to learn and determine the fuzzy parameter functions that represent network traffic behaviour. Neuro-fuzzy agents combine the features of fuzzy logic and neural networks and they have been proposed to overcome the limitations of human expertise in defining fuzzy membership functions, especially for complex
environments, such as information networks.