基于GCN的IOTA寄生链检测OA

With the deveopment of the Internet of Things(IoT)technology,transaction security in IoT environments become critical.The IOTA network,a distributed ledger technology designed specifically for IoT,plays a crucial role in managing transactions among numerous devices.Parasitic chain attacks,a common threat,undermine network security and performance by validating illegal transactions in the IOTA main tangle.A method based on graph convolutional networks(GCN)was proposed to detect parasitic chain transactions in the IOTA network.By analyzing the behavioral differences between normal and par-asitic chain transactions,four attributes were identified as feature values to capture these differences.Simulated IOTA networks containing parasitic chains were constructed based on the attack rules to gener-ate datasets,and the trained model was used to classify and identify malicious nodes.Experimental re-sults demonstrated that the model achieved over 80%accuracy in detecting malicious transactions,effec-tively identifying parasitic chain transactions in the network.