Attention integrated deep learning models for interpretable multi-class IoT intrusion detection using SHAP

The rapid growth of the Internet of Things (IoT) has increased the size, complexity, and vulnerability of network traffic, making intrusion detection a critical factor of modern cybersecurity. Traditional intrusion-detection systems (IDSs) analyze handcrafted features and rules to detect emerging attack patterns. To address these limitations, deep learning frameworks, such as attention-enhanced one-dimensional (1D) Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recu