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Adaptive Ensemble Learning Framework for Robust Intrusion Detection in Wireless Sensor Networks

Vinod Veeramachaneni

Abstract


In recent years, the security of Wireless Sensor Networks (WSNs) has faced significant challenges due to their susceptibility to various cyber threats. This study introduces an Adaptive Ensemble Learning Framework that enhances the robustness of intrusion detection in WSNs. The framework leverages a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, integrated through a dynamic voting mechanism to improve detection accuracy and reduce false positives. Experimental results on a benchmark WSN dataset demonstrated an accuracy of 97.8%, a precision of 96.3%, and a recall of 95.7%, outperforming traditional methods by an average of 5.2% in accuracy and reducing the false positive rate by 3.8%. Additionally, the framework achieved a latency reduction of 18%, ensuring real-time detection capabilities essential for WSN applications. This adaptive ensemble approach offers a highly effective and computationally efficient solution for securing WSNs, paving the way for more resilient network infrastructures against emerging threats.


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References


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