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Application of Neural Networks to Intrusion Detection
- Abstract
- 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.