Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Data mining: concepts and techniques
Data mining: concepts and techniques
A Visual Approach for Monitoring Logs
LISA '98 Proceedings of the 12th Conference on Systems Administration
NetSTAT: A Network-Based Intrusion Detection Approach
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Detecting Anomalous and Unknown Intrusions Against Programs
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
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An intrusion detection model based on Elman network is proposed to detect anomalies in network traffic. The model applies an Elman network for anomaly detection in order to provide the detector with an internal memory and therefore necessary dynamic characteristics. Unlike the existing applications of Artificial Neural Networks to detect intrusion that extract a set of attributes from only the packet headers but discard the packet payload, the present model adopts the concept of clustering the payload to alleviate information loss by retaining part of the information related to the packet payload. The model has been applied to DARPA IDS Evaluation dataset and the results demonstrate that with the two unique features, the model can identify not only intra-packet anomalies, but also inter-packet sequence anomalies.