Cybersecurity Vulnerabilities and Intrusion Detection Mechanisms in Wireless Sensor Networks: A Review

Wireless Sensor Networks (WSNs) have become integral to various sensitive and life-critical areas and applications, including environmental monitoring, healthcare, and smart cities. However, their widespread adoption raises significant cybersecurity concerns due to inherent vulnerabilities in their architecture, communication protocols, and resource constraints. This paper comprehensively analyzes security vulnerabilities specific to WSNs. Physical vulnerabilities arise from the unattended deployment of sensor nodes, making them susceptible to tampering and theft. Network-layer vulnerabilities include issues such as eavesdropping, replay attacks, and denial of service, which can severely disrupt the functionality of WSNs. Application-layer vulnerabilities often involve inadequate security measures in software, leading to data breaches and manipulation. In the face of these threats, traditional threat detection mechanisms are deficient in addressing the problem due to the inherent properties of the sensor nodes, such as limited energy, processing power, and memory. This led to the development of custom Intrusion Detection Systems (IDS) for WSNs. IDS can be classified into various types based on detection method, architecture, and deployment strategy. Additionally, this paper evaluates existing intrusion detection mechanisms designed to mitigate these vulnerabilities. We categorize these mechanisms into anomaly-based and signature-based approaches, analyzing their strengths and limitations concerning WSNs’ unique characteristics. Anomaly-based systems are adept at detecting novel attacks but may suffer from high false-positive rates, while signature-based systems offer faster detection for known threats but struggle with the emergence of new vulnerabilities. We also highlight recent advancements in machine learning and artificial intelligence as innovative approaches for enhancing intrusion detection capabilities in WSNs. These strategies promise to improve the accuracy and efficiency of intrusion detection systems by leveraging large datasets to recognize complex attack patterns. Based on our findings, this article underscores the urgent need for robust security frameworks tailored to WSN environments. This review work is aimed at providing researchers and practitioners with foundational information to aid their understanding of the security posture of wireless sensor networks.