Improved Isolation Forest-Based Anomaly Detection for Cooperative Spectrum Sensing Against SSDF Attack in CWSNs

Abstract

Cognitive radio (CR) technology aims to address spectrum scarcity by allowing sensor nodes (SNs) to detect and opportunistically access channels that are not utilized by primary users (PUs), thereby improving spectrum utilization. By applying cooperative spectrum sensing (CSS), CR improves spectrum detection accuracy, but also introduces security risks such as spectrum sensing data falsification (SSDF) attack. This letter proposes a defense strategy based on the improved isolation forest (IIF) for detecting malicious sensor nodes (MSNs) in cognitive wireless sensor networks (CWSNs). We analyze the distinct behavior patterns of the two types of SNs, and detect MSNs by isolating data points. Furthermore, we present an enhanced version of the traditional isolation forest to address its limitations. Finally, comparative experiments confirm the superiority of the proposed approach over six other anomaly detection algorithms, demonstrating the effectiveness of the improvements.