Synthesis of anomalous data to create artificial feature sets and use of same in computer network intrusion detection systems
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United States of America Patent
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Sep 3, 2013
Grant Date -
Nov 11, 2004
app pub date -
Jun 10, 2004
filing date -
Oct 25, 1999
priority date (Note) -
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Abstract
Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system. The model is trained such that it can efficiently recognize anomalous behavior by users in a dynamic computing environment where user behavior can change frequently.
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