A Novel Random Forest Adaptive Response Mechanism (RFARM) Intrusion Detection And Prevention In WSN

Abstract

This paper introduces a novel Random Forest Adaptive Response Mechanism (RFARM) for intrusion detection and prevention in Wireless Sensor Networks (WSNs). RFARM leverages the power of random forests, combining ensemble learning and adaptive response strategies to identify and thwart malicious activities effectively. The proposed mechanism continually learns from network data, adapts its response based on the severity and nature of detected intrusions, and employs a robust preventive framework. Simulation results demonstrate RFARM's superior performance in terms of detection accuracy, false positive rate, and network resilience, making it a promising solution to bolster security in WSNs.