Enhancing Security Features in Wireless Sensor Networks using AI based Deep Learning Methods

Wireless sensor networks (WSNs) are crucial for a wide range of applications. However, their open architecture and limited resources make them vulnerable to intrusion. Improve the security of WSNs by incorporating advanced deep learning methods, including RNNs, Autoencoders, and Convolutional Neural Networks (CNNs). When it comes to identifying intrusions, CNNs need to analyze spatial patterns in data, while RNNs are responsible for managing temporal relationships in sequential data. Autoencoders are commonly used in anomaly detection to identify typical data patterns. By training on preprocessed sensor data, these deep learning models ensure network availability and data integrity. They enable the immediate detection and handling of potential dangers. Evaluation metrics denote that in simulated assault scenarios, CNNs, RNNs, and autoencoders have achieved impressive accuracy rates of 95.2%, 97.8%, and 97.5%, respectively which showcases their effectiveness when compared to previous techniques. The proposed design emphasizes adaptability and resilience, offering WSNs robust protection against emerging cyber threats.