Intrusion Detection System on IoT with 5G Network Using Deep Learning

Abstract

The Internet of Things (IoT) cyberattacks of fully integrated servers, applications, and communications networks are increasing at exponential speed. As problems caused by the Internet of Things network remain undetected for longer periods, the efficiency of sensitive devices harms end users, increases cyber threats and identity misuses, increases costs, and affects revenue. For productive safety and security, Internet of Things interface assaults must be observed nearly in real time. In this paper, a smart intrusion detection system suited to detect Internet of Things-based attacks is implemented. In particular, to detect malicious Internet of Things network traffic, a deep learning algorithm has been used. The identity solution ensures the security of operation and supports the Internet of Things connectivity protocols to interoperate. An intrusion detection system (IDS) is one of the popular types of network security technology that is used to secure the network. According to our experimental results, the proposed architecture for intrusion detection will easily recognize real global intruders. The use of a neural network to detect attacks works exceptionally well. In addition, there is an increasing focus on providing user-centric cybersecurity solutions, which necessitate the collection, processing, and analysis of massive amounts of data traffic and network connections in 5G networks. After testing, the autoencoder model, which effectively reduces detection time as well as effectively improves detection precision, has outperformed. Using the proposed technique, 99.76% of accuracy was achieved.