A Review on Dynamic Cluster-Based Intrusion Detection System in Wireless Sensor Networks with Deep Learning

Abstract

Deep learning (DL) models have shown remarkable effectiveness and accuracy in detecting security threats within wireless sensor networks (WSNs). The increasing dependence on mobile and distributed communication systems has intensified cybersecurity challenges due to the rising frequency, complexity, and sophistication of attacks. Traditional security frameworks often struggle to detect advanced or concealed malware, underscoring the need for intelligent and adaptive solutions. This study intends to review, analyze, and categorize the latest advancements in DL-based approaches to cybersecurity in WSNs. It aims to identify major attack types, evaluate existing DL defense mechanisms, assess computational overhead, and highlight challenges related to data scarcity and real-time implementation. The analysis emphasizes how DL algorithms improve intrusion detection, malware classification, and anomaly recognition in resource-limited WSN environments. The findings provide a structured understanding of current methodologies and potential directions for developing robust and scalable DL-driven security frameworks for WSN applications.