Information privacy protection in malicious node detection in wireless sensor networks

Wireless sensor networks are widely used due to their self-organisation and low power consumption but are vulnerable to malicious attacks and privacy leaks. This study proposes a security solution combining trust management mechanisms, extreme gradient boosting, and differential privacy. Suspicious nodes are identified using trust values, classified with extreme gradient boosting, and protected through differential privacy. In a network of 8,000 nodes, the model achieves a computation time of 512 ms, reducing by 412 ms compared to traditional deep learning models, with CPU utilisation below 48%. Against selective forwarding attacks, it attains 93.2% detection accuracy with a 4.8% false positive rate. This approach enhances WSN security by providing efficient attack detection and robust privacy protection, significantly improving network resilience against cyber threats.