Incremental Update Intrusion Detection for Industry 5.0 Security: A Graph Attention Network-Enabled Approach
Yixuan Wu, Laisen Nie, Xuanrui Xiong, Balqies Sadoun, Lin Yang, Zhaolong Ning
Abstract
The Industry 5.0 (I5) incorporates numerous emerging technologies that enable the perception, management, interaction, and control of real physical objects. As cyber attacks are becoming more intelligent and accessible, I5 security issues are gaining increasing prominence. An intrusion detection system plays an important role in network security by detecting and identifying various security threats. However, owing to the limited computing and storage resources of users, it is difficult for I5 nodes to support large-scale network data collection and simultaneous transmission of the collected data. To address the above problems, this study investigates an intelligent intrusion detection technology for I5 security and develops a low-loss intrusion detection algorithm based on abnormal attacks. We first establish an incremental update feature selection method, which realises feature selection using a fuzzy rough set and a fuzzy knowledge distance. Subsequently, to reduce the decision loss of the selected features, we implement feature correlation using the fuzzy knowledge distance. Based on the selected features, we achieve incremental update intrusion detection using a graph attention network and a random forest. Simultaneously, we prove the convergence of the designed graph attention network in a specific scene. Finally, the experimental results show that our designed method has higher accuracy than existing methods.