Intrusion Detection using Neural Networks: a Grid Computing based Data Mining Approach

Scientific disciplines such as life sciences as well as security and business fields depend on Knowledge Discovery because of the increasing amount of data being collected and for the complex analyses that need to be performed on them. New techniques, such as parallel, distributed, and grid-based data mining, are often able to overcome some of the characteristics of current data sources such as their large scale, high dimensionality, heterogeneity, and distributed nature. In several of these data mining applications, neural networks can be successfully applied. Moreover, an approach using neural networks seems to be one of the most promising methods for intrusion detection in a computer system or network security today. In this paper we describe a grid computing data mining approach for an intrusion detection application based on neural networks. Detection is carried out through the analyses of internet traffic generated by users in a network computer system.