Application of Neural Networks to Intrusion Detection

Intrusion Detection Systems ( IDS ) are now mainly employed to secure company networks. Ideally, an IDS has the capacity to detect in real-time all ( attempted ) intrusions, and to execute work to stop the attack ( for example, modifying firewall rules ). We present in this paper a ' state of the art ' of Intrusion Detection Systems developing commercial and research tools and a new way to improve false-alarm detection using Neural Network approach. This approach is still in development nevertheless it seems to be very promising for the future. This paper is organized as follows : first we present the global architecture of IDS and a few commercially available tools then we analyze new axes of research to improve IDS's performances and particularly the application of Neural Networks to Intrusion Detection.