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Wireless Sensor Networks (WSNs) have been securing a big position in the new aspect of security network attacks, where these suffer from various serious cyber threats that can play with their data integrity and reliability. Due to the key importance of WSN in a wide spectrum range of applications such as environmental monitoring and military field, building reliable, robust and efficient intrusion detection systems (IDS) is necessary. Although traditional machine learning approaches have been intended to detect these threats, they often lack high accuracy due to the complexity and dimensionality of WSN data. To address these limitations, the study introduces an innovative approach that greatly improves intrusion detection performance in WSNs by combining a high-speed deep learning model with sophisticated feature selection methods. The newly developed system underwent extensive testing using the WSN-DS dataset and applied Gaussian Naive Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms within the machine learning framework. The outcomes were exceptional, demonstrating a flawless accuracy rate of 100% and representing a significant advancement compared to prior methodologies based solely on traditional machine learning techniques. The study illustrates how the fusion of deep learning and optimized feature selection effectively addresses the distinctive challenges presented by WSN environments. The results not only present a highly precise and effective method for intrusion detection but also lay the groundwork for further research focused on fortifying the security of sensor networks against progressively intricate cyber threats.
Saleh et al. (Thu,) studied this question.