Intrusion Detection System Using Pca

Abstract

Software-Defined Networking is an innovative architecture approach in the networking field. This technology allows networks to be centrally and intelligently managed by unified applications such as traffic classification and security management. Traditional networks’ static nature has a minimal capacity to meet organizations’ business requirements. Software-Defined Networks (SDNs) are emerging architectures that address a range of networking challenges with new solutions. Nevertheless, these centralized and programmable techniques face various challenges and issues that require contemporary security solutions such as Intrusion Detection Systems. Recently, the majority of this type of security solution has been developed using Machine Learning techniques. Deep Learning algorithms have recently been used to provide more accuracy and efficiency. This paper presents a new detection approach based on Convolutional Neural Network (CNN). The experiments proved that the proposed model could be successfully implemented in a Software-Defined Network controller to detect various attacks with 100% accuracy, and achieved a low degradation rate of 2.3% throughput and 1.8% latency when executed in a large-scale network.