Neural Networks in Statistical Anomaly Intrusion Detection
نویسندگان
چکیده
In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; PerceptronBackpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test various neural networks with different hidden neurons. Our results showed that the classification capabilities of BP and PBH outperform those of other neural networks. Key-Words: Security, Intrusion Detection, Statistical Anomaly Detection, Neural Network Classification, Perceptron, Backpropagation, Perceptron-Backpropagation-Hybrid, Fuzzy ARTMAP, Radial-Based Function
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