Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future
Ankita Sharma, Shalli Rani · 2025
Several industries are expected to experience significant changes with the introduction of 5G technology, which will offer improved connectivity, reduced latency, and greater speeds. The frequency and complexity of cyber attacks targeting interconnected servers, applications, and communication networks in the Internet of Things (IoT) are increasing. The persistent problems with the IoT network undermine the functionality of susceptible devices, consequently heightening the risk of identity theft and cyber attacks, resulting in extra costs and reduced profitability. Ensuring safety and security relies on promptly monitoring attacks on IoT interfaces. This paper presents the creation of a sophisticated intrusion detection system that utilises the 5G Network. A deep learning (DL) algorithm was employed to detect dangerous network activity in the IoT. The identification solution enables the compatibility of several IoT connectivity standards while preserving operational security. An Intrusion Detection System (IDS) is a network security technology employed to safeguard the network. The global dangers discovered using the suggested intrusion detection architecture are easily identifiable, as illustrated in our study. Neural networks possess exceptional ability in identifying and detecting attacks. Moreover, there is a growing demand for cyber security solutions that give priority to the requirements and choices of users. This requires the gathering, examination, and manipulation of massive amounts of network connections and data flow within 5G networks.