Edge Intelligence (EI)-Enabled Malware Internet of Things (IoT) Detection System

With the development of 5G networks, IoT devices are increasingly used in the industrial and household fields. Due to the characteristics of the CPU multi-architecture system of IoT devices, the traditional signature-based and single-architecture based detection methods is not effective are for detecting cross-architecture malware. To solve this problem, we propose a cross-architecture IoT malware detection system based on Graph Attention Networks (GAT). We employ the CFG extracted from the binary executable file as the graph structure and Opcode and PSI as the feature attributes of the graph nodes. Through GAT, we obtain the neighborhood features of each node, and assign different weights to different nodes in the neighborhood and finally complete detection. Different training phases are allocated to the edge and cloud center for execution to improve system performance and protect user data privacy. The experimental results of par show that the detection accuracy of our system reaches 99.67%. Compared with existing detection methods, we obtained the best accuracy.