Fast learning neural network intrusion detection system

Abstract

Assuring the security of networks is an increasingly challenging task. The number of online services and migration of traditional services like stocktrading and online payments to the Internet is still rising. On the other side, criminals are attracted by the values of business data, money transfers, etc. Therefore, safeguarding the network infrastructure is essential. As Intrusion Detection Systems (IDS) had been in the focus of a numerous of researches for the last years, several sophisticated solutions had been found. Very capable IDS are based on neural networks. However, these systems lack of an adaptability to dynamic changing environments or require a protracted learning phase before they are operational. The approach is to overcome these restrictions by introducing a modular neural network based on pre-processed components supplemented by static policies. By that, it is possible to overcome long-lasting learning phases. © 2009 IFIP International Federation for Information Processing.