Robust Paradigm for Industrial IoT Networks: A Deep Learning-Driven Security Framework for Threat Detection and Mitigation

Cyber security vulnerabilities have emerged in critical infrastructure because IIoT devices for industrial operations continuously integrate into existing systems. The IIoT network security requires immediate attention because these systems experience multiple cyber threats from Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks in addition to sophisticated malware which threaten operational safety. The extensive features of IIoT networks present difficulties in securing them because these operational environments contain numerous devices which transmit abundant data through restricted resources while using standard security systems that no longer function effectively. Securing IIoT networks has grown difficult because attackers use new methods and operations need immediate response times. The current security solutions fail to deliver satisfactory results because they produce excessive false alerts while detecting elements slowly and cannot process the enormous data volume coming from IIoT devices. The wide assortment of intrusions alongside their irregular attack patterns mandate sophisticated detection methods which recognize established and previously unknown security threats. This framework operates with IIoT network requirements to give real-time scalability which boosts industrial application security. The RNN+MLP model demonstrates effective threat detection in IIoT environments according to experimental results which outperform existing methods for IIoT security.