Published Paper Details:

The adoption of the Industrial Internet of Things (IIoT) and the deployment of 5G technology have led to significant advancements in industrial processes and automation. However, this proliferation of interconnected devices within the context of 5G-enabled IIoT networks has also exposed industrial systems to a heightened risk of cyberattacks. Securing IIoT networks in the era of 5G has thus become a paramount concern for ensuring the integrity, availability, and safety of critical industrial systems. This paper presents a comprehensive exploration of machine learning approaches for detecting and mitigating network attacks in the context of the 5G-enabled IIoT. IIoT deals with large scale networks and known as use of internet of things for industrial applications. In manufacturing the networked devices, interconnected sensors and computer systems used are together refereed as Industrial IoT. Maintaining data privacy and securing the IIoT network is also a great challenge. Using various Artificial Intelligence algorithms, it is possible to provide the security to the IIoT network and data against the possible attacks on the IIoT network. This paper provides the systematic survey of possible attacks on industrial IoT network, applied ML technique for detection of attacks along with the suitable platform used.