Enhancing intrusion detection in wireless sensor networks through deep hybrid network empowered by SC-attention mechanism

Wireless Sensor Networks (WSNs) are frequently deployed in environments that are either unattended or hostile, exposing them to a variety of attack types. It is vital to secure WSNs, particularly when they are monitoring sensitive or critical data. Utilizing an Intrusion Detection System (IDS) can aid in identifying unauthorized access or harmful activities in the network. In the domain of Network Intrusion Detection Systems (NIDS), conventional methods have limitations in detecting new threats and unknown attack patterns efficiently. Addressing these issues, this study introduces a new method known as the Deep Hybrid Network with Spatial and Channel Attention (DHN-SCA). This method merges deep learning techniques with attention mechanisms. The DHN employs Convolutional Neural Networks (CNNs) alongside a Local Attention Module to improve the precision and effectiveness of intrusion detection. The Local Attention Module comprises two components: spatial attention and channel attention. Spatial attention uses average pooling on the feature tensor, and Channel Attention incorporates both global average pooling and global max pooling, followed by fully connected layers. These components refine the feature tensor by element-wise multiplication with the original features. The performance of the DHN is tested and evaluated using benchmark datasets. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to gauge the DHN’s performance against other intrusion detection methods.