An IoT-Based 5G Wireless Sensor Network Employs a Secure Routing Methodology Leveraging DCNN Processing

Abstract

The security principles of the fifth generation (5G) are anticipated to include robust cryptography models, information security models, and machine learning (ML) powered Intrusion Detection Systems (IDS) specifically designed for Internet of Things (IoT) based wireless sensor networks (WSNs). Nevertheless, the existing security models fall short in addressing the dynamic network characteristics of WSNs. In this context, the suggested system introduces a secure and collaborative multi-watchdog system through the implementation of deep convolutional neural network (DCNN) and distributed particle filtering evaluation scheme (DPFES). The proposed system utilizes deep learning (DL) techniques to create a dynamic multi-watchdog system that safeguards each sensor node by monitoring its transmissions. Furthermore, the proposed approach includes secure data-centric and node-centric evaluation methods that are crucial for enhancing the security of 5G-based IoT-WSN networks. The network evaluation processes based on DL facilitate the creation of a secure multi-watchdog system within dense IoT-WSN environments. This system enables the deployment of active watchdog IDS agents as needed. The proposed approach includes various components such as a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. From a technical perspective, the system is driven by the implementation of DPFES, which utilizes particle filtering frameworks to analyze network events and establish a secure 5G environment. The system has been successfully implemented, and its results have been compared with those of other similar works. The performance of the proposed cooperative multi-watchdog system demonstrates a significant improvement of $$ and $$ compared to other techniques.