Wireless Sensor Networks (WSNs) are widely used in applications such as environmental monitoring, healthcare, and industrial automation. However, WSNs are highly vulnerable to malicious attacks due to their limited resources and wireless nature. This paper proposes a Deep Learning-based Intrusion Detection System (IDS) for WSNs to enhance network security. The proposed system leverages a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models to detect anomalies and malicious activities with high accuracy. Experimental results demonstrate that the proposed approach outperforms traditional machine learning methods in terms of detection accuracy, false positive rate, and computational efficiency. to further improve performance, the system incorporates data preprocessing and feature extraction mechanisms to handle noisy and imbalanced datasets effectively. The proposed IDS exhibits strong generalization ability, making it suitable for large-scale and dynamic WSN environments. This work contributes to the development of robust, intelligent, and adaptive security frameworks capable of safeguarding resource constrained wireless networks against emerging threats.
Wireless Sensor Networks, Intrusion Detection System, Deep Learning, CNN, LSTM, Network Security, Anomaly Detection, Cyber Threats, Data Security.
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