Wireless Sensor Networks (WSNs) have become a critical area of research in computer science due to their extensive applications across both military and civilian domains. Typically comprising numerous resource-constrained sensor nodes that collect and relay data to a central processing unit, WSNs face unique security challenges arising from their limited computational power, diverse deployment strategies, and reliance on multi-hop communication. Ensuring the confidentiality and integrity of transmitted data requires robust protection against unauthorized access and cyber threats. To address these challenges and enhance the reliability of WSN operations, this study proposes an advanced intrusion detection system (IDS) based on Deep Neural Networks (DNNs). While traditional IDS approaches leveraging machine learnin g often underperform with imbalanced datasets, the proposed method improves detection accuracy by employing a cross-correlation technique to extract the most relevant features from the WSNDShacking dataset. A tailored DNN architecture is then trained using these optimized features to accurately detect and classify various types of network intrusions. Experimental results demonstrate the effectiveness of the proposed model, achieving an accuracy of 95.84%, a precision of 96.41%, and a recall of 94.27%. Comparative analysis confirms that the DNN-based IDS outperforms both conventional machine learning and existing deep learning models, making it a robust solution for securing WSN environments.
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