Title: Information privacy protection in malicious node detection in wireless sensor networks

Authors: Tao Chen

Addresses: School of Health and Elderly Care, Shandong Women's University, Ji'nan, 250000, China

Abstract: 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.

Keywords: wireless sensor networks; WSNs; malicious node detection; privacy protection; extreme gradient boosting; XGBoost; differential privacy.

DOI: 10.1504/IJICS.2026.150535

International Journal of Information and Computer Security, 2026 Vol.29 No.1, pp.44 - 62

Received: 21 Mar 2025
Accepted: 30 Jul 2025
Published online: 16 Dec 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article