Proposal of security architecture in 5G mobile network with DDoS attack detection

Abstract

Software-Defined Networking or SDN (Software-Defined Networking) is a technology for software control and management of the network in order to improve its properties. Unlike classic network management technologies, which are complex and decentralized, SDN technology is a much more flexible and simple system. The new architecture may be vulnerable to several attacks leading to resource depletion and preventing the SDN controller from providing support to legitimate users. One such attack is the Distributed Denial of Service (DDoS), which is on the rise today. We suggest Modified-DDoSNet, a system for detecting DDoS attacks in the SDN environment. A model based on Deep Learning (DL) techniques will be implemented, combining a Recurrent Neural Network (RNN) with an Autoencoder. The proposed model, which was first trained to detect attacks, was implemented in the security architecture of the SDN network, as a new component. The security architecture of the SDN network contains a total of 13 components, each of which represents an individual part of the architecture, where the first component is the RNN - autoencoder. The model itself, which is the first component, was trained in the CICDDoS2019 dataset. It has high reliability for attack detection, which increases the security of the SDN network architecture.