Training And Testing Anomaly-Based Neural Network Intrusion Detection Systems

Abstract

Networks are up against detecting dynamic and unknown threats. Anomaly-based neural network (NN) intrusion detection systems (IDSs) can manage this if trained and tested accordingly. This requires the IDS to be evaluated on how well it can detect these intrusions. Evaluating NN IDSs can be a complex and difficult task. One needs to be able to measure the convergence rate and performance (detection and failure) rate of the IDS. This paper explores the different methods used by researchers to train and test their IDS models. It also found that the data used can effect the results of training and testing the NN IDS models.

Keywords

• intrusion detection, neural network, KDD 99, convergence rate, performance rate

References

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