Rule extraction from neural networks for intrusion detection in computer networks

In many neural network applications, the understanding of the network functionality is an important issue. Trained neural networks are often termed as "black boxes" which do not allow to get a deeper insight into the relationships between the input (feature) and output spaces. In the past years some researchers addressed the problem of rule extraction from trained neural networks. This article investigates the properties and results of different rule extraction techniques in a specific application area: The detection of intrusions in computer networks. This application area is chosen because intrusion detection and the development of appropriate intrusion detection systems (IDS) gains more and more importance with the rapidly increasing impact of the Internet.