Comparative Study of Intrusion Detection Techniques in Wireless Sensor Networks

Shanmugavadivu C

Abstract


The rapid expansion of Internet of Things (IoT) environments and Wireless Sensor Networks (WSNs) has significantly increased exposure to diverse cyber threats. Traditional Intrusion Detection Systems (IDS) often fail to cope with the dynamic, large-scale and resource-constrained nature of the networks. Recent research highlights the growing adoption of Machine Learning (ML), Deep Learning (DL) and computational intelligence techniques are used to enhance detection of accuracy, adaptability, and efficiency. This survey papers presents a comprehensive review of ML-based Intrusion Detection approaches for WSNs and IoT, focusing on feature selection, dimensionality reduction, hybrid and ensemble models, optimization techniques, and emerging trends.

Keywords


Wireless Sensor Networks, Internet of Things, Intrusion Detection Systems, Machine Learning, Deep Learning, Feature Selection

References


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