5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques

TL;DR

This paper demonstrates that deep learning models, specifically CNN and FNN, can effectively detect and mitigate DDoS attacks in IoT devices operating within 5G networks, achieving 99% accuracy.

Contribution

The study applies CNN and FNN algorithms to a specialized IoT-5G dataset, showing their high effectiveness in security threat detection.

Findings

Both CNN and FNN achieved 99% accuracy in DDoS detection.

The constructed 5G IoT network simulation validated the deep learning approaches.

Deep learning enhances IoT security in 5G environments.

Abstract

The development and implementation of Internet of Things (IoT) devices have been accelerated dramatically in recent years. As a result, a super-network is required to handle the massive volumes of data collected and transmitted to these devices. Fifth generation (5G) technology is a new, comprehensive wireless technology that has the potential to be the primary enabling technology for the IoT. The rapid spread of IoT devices can encounter many security limits and concerns. As a result, new and serious security and privacy risks have emerged. Attackers use IoT devices to launch massive attacks; one of the most famous is the Distributed Denial of Service (DDoS) attack. Deep Learning techniques have proven their effectiveness in detecting and mitigating DDoS attacks. In this paper, we applied two Deep Learning algorithms Convolutional Neural Network (CNN) and Feed Forward Neural Network (FNN) in dataset was specifically designed for IoT devices within 5G networks. We constructed the 5G network infrastructure using OMNeT++ with the INET and Simu5G frameworks. The dataset encompasses both normal network traffic and DDoS attacks. The Deep Learning algorithms, CNN and FNN, showed impressive accuracy levels, both reaching 99%. These results underscore the potential of Deep Learning to enhance the security of IoT devices within 5G networks.