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Min–Max Hyperellipsoidal Clustering For Anomaly Detection In Network Security, Suseela T. Sarasamma, Qiuming Zhu
Min–Max Hyperellipsoidal Clustering For Anomaly Detection In Network Security, Suseela T. Sarasamma, Qiuming Zhu
Computer Science Faculty Publications
A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is …