Intrusion Detection Systems in Wireless Sensor Networks: A Comprehensive Literature

Abstract

This comprehensive literature review examines the evolution and current state of intrusion detection systems (IDS) in wireless sensor networks (WSN) from 2019 to 2024. The analysis synthesizes findings from 30 highly relevant scholarly publications, revealing significant trends in methodologies, architectures, and performance outcomes. Key findings indicate a clear shift toward hybrid detection approaches that combine anomaly-based and signature-based techniques, increasing adoption of machine learning and deep learning algorithms, and growing emphasis on energy-efficient distributed architectures. The review identifies that modern IDS solutions achieve detection rates ranging from 77% to 98.6%, with false positive rates as low as 0.9% to 2%. However, persistent challenges remain in balancing detection accuracy with energy consumption, addressing resource constraints inherent to WSN environments, and detecting sophisticated zero-day attacks. This review provides researchers and practitioners with a structured understanding of current methodologies, performance benchmarks, and future research directions in WSN intrusion detection