SDCBM: A Secure Data Collection Model With Blockchain and Machine Learning Integration for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) often struggle with managing extensive data volumes, given their resource-constrained nature. Deployed in unattended areas, they face significant security risks and attacks. This study introduces the secure data collection model with blockchain and machine learning integration for WSNs (SDCBM), designed to identify intrusions and ensure secure data collection and storage for WSN applications. SDCBM employs an extreme learning machine (ELM) model, a fast single-layer feedforward neural network (NN), and integrates techniques for balancing the data distribution and selecting relevant features to enhance real-time detection of malicious attacks. Data is preprocessed and balanced utilizing the synthetic minority oversampling technique (SMOTE) and Tomek-Links combination method. To enhance the feature selection process, the Harris Hawk optimization (HHO)-based method is proposed. The blockchain module manages network node registration, authentication, node revocation, and secure storage of data hashes and node credentials. Simulation results demonstrate the efficacy of the proposed SDCBM method in detecting malicious nodes and enhancing secure data collection, thereby strengthening the security of WSNs.