Classifier Design Using Adaptive GHA Neural Networks

An adaptive Generalized Hebbian Algorithm (GHA) is presented which can be used to approach the intrinsic dimension of an input data set. The classifier design based on adaptive GHA networks is given in detail and the determination method of the classifier parameters is also described. The classifier can be trained by using supervised manner. We applied this approach to the domain of intrusion detection. Some simulations are carried out for anomaly detection by using labeled normal type network connections, and the misuse detection are performed on specified type attacks of denial-of-service intrusions. All the training and testing datasets are based on the KDD CUP 1999 intrusion evaluation data set. Performance comparisons are also made with other recent published methods.