Data clustering optimization using support vector machines

Abstract

The analysis of massive data is becoming more and more critical. One of the systems that process real-time data are computer networks. The data flowing through these networks is enormous and requires technicality to manage it better, and the most central characteristics of these systems is to ensure security. To ensure this task, administrators use intrusion detection systems (IDSs). The major problems with these systems are the false positive and the speed of the system to process data and analyze it. For this, we present an optimization of the existing methods based on artificial neural networks, through combining two machine learning procedures; unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system.