Application of Improved SOM Neural Network in Anomaly Detection

Abstract

For the false alarm rate, false negative rate, training time and other issues of SOM neural network algorithm, the author Gives an improved anomaly detection SOM algorithm---FPSOM through the introduction of the learning rate, which can adaptively learn the original sample space, better reflects the status of the original data. At the same time, combined with the artificial neural network, The author also gives the intelligent detection model and the model of the training module, designed the main realization of FPSOM neural network algorithm, and finally simulation experiments were carried out in KDDCUP data sets. The experiments show that the new algorithm is better than SOM which can greatly shorten the training time, and effectively improve the detection rate and reduce the false positive rate.