Anomaly Detection

Network-Based Anomaly Detection Using Discriminant Analysis

ABSTRACT

Anomaly-based Intrusion Detection Systems (IDS) can be a valuable tool for detecting novel network attacks. This paper analyzes the use of linear and non-linear discriminant analysis on packet header information from Transport and Internet layers of the TCP/IP model to classify packets as normal or abnormal. By training on normal traffic for a particular service (web and secure shell) and known attacks, the classifier can automatically identify differences between packets that may be used to classify future unknown traffic.

Published In

Volume 1, Issue 2

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

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