Design of iterative convolutional neural network model for malicious node detection and optimization in wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) represent a critical advancement in modern communication technologies, characterized by distributed sensor nodes that communicate wirelessly to monitor and report environmental conditions. These networks are integral to applications ranging from military surveillance and environmental monitoring to smart grids and intelligent transportation systems. The reliability and security of WSNs are paramount, as they operate in dynamic and often hostile environments where nodes can be susceptible to various attacks, including malicious node injections. Therefore, this research develops a novel model—an Iterative Convolutional Neural Network-based Malicious Node Detection (ICNNMND)—that provides an innovative three-phase method to enhance security and performance in WSNs. In the First phase of work, it collects the sample node dataset and performs pre-processing with malicious node detection utilising machine learning techniques. A novel algorithm called Advanced Dataset Pre-processing Algorithm (ADPA) was developed for pre-processing of nodes and Malicious Node Detection with Machine Learning Secure Learning (MND-MLSL) algorithm for handling the data preparation, attributes of nodes, topologies to be followed and the communication packets to be normalised for better transmission. The machine learning models were utilised based on the neural network schema and the experiments were completed. The results in first phase showed that the packets delivery of 21 instances out of 100 were found to be stolen and delayed with the execution time of 185.5513 seconds, stolen rate of 2% and total energy consumption of 562.7417 joules respectively. The packet delivery rate was 98% in the absence of malicious nodes. In the second phase, Iterative Convolutional Neural Network based Malicious Node Detection (ICNNMND) was designed which identifies, the complex patterns during data transmission to perform classification and also maximises pool layers and fully connected layers to enhance robustness using dropout regularisation technique. The accuracy was found to be 97.3155% after 100 iterations with good results in precision, recall and F1-Score respectively. The final third phase of work deployed the optimisation of ICNNMND using novel Network Hybrid Optimization Algorithm (NHOA) that enhances latency, reception quality and throughput for better optimisation. The results showed improvements in the overall latency and other significant parameters like Bit Error Rate (BER) getting reduced from 10-4 to 10-9

over 30 runs, indicating enhanced transmission accuracy and reduced errors. Throughput analysis also showed an enhanced output of 98% after 50 epochs, highlighting the model's effectiveness in maximizing data transfer efficiency. The model presented in the research showed that by combining machine learning with different optimisation and deep learning models, the security of data transmission can be enhanced with the high performance of WSNs.