Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks

Fifth Generation (5G) security principles are widely expected with effective cryptography models, information security models, Machine Learning (ML) based Intrusion Detection systems (IDS) for Internet of Things (IoT) based Wireless Sensor Networks (WSN). However, the current security models are insufficient against the dynamic network nature of WSNs. On this scope, the proposed system develops Deep Convolutional Neural Network (DCNN) and Distributed Particle Filtering Evaluation Scheme (DPFES) for constructing a secure and cooperative multi-watchdog system. The proposed Deep Learning (DL) based dynamic multi-watchdog system protects each sensor node by monitoring the node transmission. In addition, the proposed work encompasses secure data-centric and node-centric evaluation procedures that are required for expanding the secure medium of 5G-based IoT-WSN networks. The DL-based network evaluation procedures drive the entire network to build a secure multi-watchdog system that enables on-demand active watchdog IDS agents among dense IoT-WSN. Notably, the proposed work contains a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. Based on technical aspects, the proposed system is motivated to implement DPFES to analyze network events using particle filtering frameworks to build a secure 5G environment. The system is implemented and results are compared with related works. The performance of the proposed cooperative multi-watchdog system delivers 10% and 15% of better results than other techniques.