  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.
