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RAID
2004
Springer

Anomalous Payload-Based Network Intrusion Detection

9 years 11 months ago
Anomalous Payload-Based Network Intrusion Detection
We present a payload-based anomaly detector, we call PAYL, for intrusion detection. PAYL models the normal application payload of network traffic in a fully automatic, unsupervised and very effecient fashion. We first compute during a training phase a profile byte frequency distribution and their standard deviation of the application payload flowing to a single host and port. We then use Mahalanobis distance during the detection phase to calculate the similarity of new data against the pre-computed profile. The detector compares this measure against a threshold and generates an alert when the distance of the new input exceeds this threshold. We demonstrate the surprising effectiveness of the method on the 1999 DARPA IDS dataset and a live dataset we collected on the Columbia CS department network. In once case nearly 100% accuracy is achieved with 0.1% false positive rate for port 80 traffic.
Ke Wang, Salvatore J. Stolfo
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where RAID
Authors Ke Wang, Salvatore J. Stolfo
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