Neural network approach to real-time network intrusion detection and recognition

Modern intrusion detection systems process large amounts of data. Most systems use signature- and rule-based approaches to find attack traces. The main disadvantage of such technologies is the need of continuous updating of signature database to let the system detect newest attacks. We present recirculation neural network based approach which lets to detect previously unseen attack types in real-time mode and to further correct recognition of this types. The experiments held on both KDD data and real network traffic data prove that this approach can be used in host-based anomaly and misuse detectors.