Malicious Node Detection and Secure Data Storage in Wireless Sensor Networks Using Blockchain and Machine Learning

Wireless Sensor Networks (WSNs) are widely used for monitoring and data collection in various environments. However, these networks are vulnerable to attacks from malicious nodes, which can compromise the integrity and reliability of the system. In this work, I propose a model that combines blockchain-based registration and authentication with machine learning for real-time malicious node detection. The system uses the Histogram Gradient Boost (HGB) classifier to identify threats and stores legitimate data in the Interplanetary File System (IPFS), with hashes recorded on the blockchain. To keep the system efficient, I use the Verifiable Byzantine Fault Tolerance (VBFT) consensus instead of Proof of Work (PoW). My results, based on the WSN-DS dataset, show that this approach not only improves detection accuracy but also reduces transaction costs compared to traditional methods