AI-driven robust dual attention-enhanced intrusion detection framework for IoT devices in edge-cloud computing networks

Intrusion detection is a critical aspect of ensuring network security, especially in Internet of Things (IoT) environments, where the complexity and volume of data present unique challenges. In this context, this paper presents an AI-driven robust intrusion detection framework for intrusion detection in the IoT environment. The detection module in the proposed approach is implemented in the edge layer to detect malicious traffic at an early stage. The attack detection module of the proposed approach integrates channel attention and spatial attention mechanisms to enhance feature extraction and detection precision. The channel attention block of the detection module leverages global average pooling, max pooling, and a shared multilayer perceptron to emphasize interchannel dependencies, while the spatial attention block refines spatial feature maps using convolutional operations on aggregated pooling output. Feature optimization is achieved through the Success History Intelligent Optimizer (SHIO), which reduces features from 41 to 28. The proposed framework achieves 98.4 % accuracy while utilizing 95.7 % fewer trainable parameters compared to traditional approaches like LSTM-based models, offering an efficient and scalable solution for attack detection in IoT environment.