Integrating Artificial Intelligence & Deep Learning Hybridization For Optimization Of Secure Routing In The Critical Infrastructure Of The Internet Of Things (Iots) With Intrusion Detection Capability Based On Software-Defined Network (Sdn) A

The invention of deep learning in secure routing triggered an exceptional expansion of the Internet of Things (IoTs). Concurrent analysis of human body data occurs in the fog layer after sensors and actuators collect information from smart medical devices. The combination of criticality with increased complexity and dynamic capabilities causes H-IoT devices to be incompatible with typical network configurations which generates security and QoS problems. The identification of appropriate fog nodes together with unnecessary data reduction proves to be a complicated process. This paper incorporates SDN-driven DL to design a secure and intelligent framework for H-CIoT networks which solves existing network challenges. The SDN architecture stands out as a suitable solution because it enables network infrastructure reconfiguration while managing distributed IoT network architecture through separate data and control planes. The Proposed ML based AODV and AOMDV offers enhanced network security through centralized control and programmability, allowing for fine-grained security policies and real-time adjustments. The AOMDV security module serves as an implementation to detect multiple attack types that appear in the IoT network. The training process of the Deep Learning model utilizes IOT devices archival data in industry. The system uses acquired information to determine if data needs to be transferred to the fog layer. The suggested framework utilizes deep learning hybridization and CNN for selecting the optimal fog node alongside its features. The simulation of the proposed framework demonstrated 99.59% accuracy and achieves 80% detection ratio together with a 0.99% ideal throughput and packet delivery rate of 0.89%, a minimum energy of 0.11 m joules, at a maximum speed of 0.84 bps, and a negligible delay of 0.3415 ms when tested with 30 nodes. alongside 4% increased F1-score performance at 10 ms faster latency and lower energy usage of 25 W and 0.66% better probability. Keywords:Machine Learning, ResNet, Deep Neural Network, CNN, Prediction Models, Hybrid Machine Learning, Routing Attacks Detection