Biblio

In this paper we solve the problem of neural network technology development for e-mail messages classification. We analyze basic methods of spam filtering such as a sender IP-address analysis, spam messages repeats detection and the Bayesian filtering according to words. We offer the neural network technology for solving this problem because the neural networks are universal approximators and effective in addressing the problems of classification. Also, we offer the scheme of this technology for e-mail messages “spam”/“not spam” classification. The creation of effective neural network model of spam filtering is performed within the databases knowledge discovery technology. For this training set is formed, the neural network model is trained, its value and classifying ability are estimated. The experimental studies have shown that a developed artificial neural network model is adequate and it can be effectively used for the e-mail messages classification. Thus, in this paper we have shown the possibility of the effective neural network model use for the e-mail messages filtration and have shown a scheme of artificial neural network model use as a part of the e-mail spam filtering intellectual system.

This paper describes the technology of neural network application to solve the problem of information security incidents forecasting. We describe the general problem of analyzing and predicting time series in a graphical and mathematical setting. To solve this problem, it is proposed to use a neural network model. To solve the task of forecasting a time series of information security incidents, data are generated and described on the basis of which the neural network is trained. We offer a neural network structure, train the neural network, estimate it's adequacy and forecasting ability. We show the possibility of effective use of a neural network model as a part of an intelligent forecasting system.