Intrusion Detection System Using PCA with Random Forest Approach

Abstract— 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 Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model 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. This work contributes to the development of robust and intelligent security mechanisms for WSNs. Wireless Sensor Networks, Intrusion Detection System, Deep Learning, CNN, LSTM, Network Security.