Research on Fine-grained Detection Method of Honey Pot Contracts Based on LSTM and Fuzzing - Xi, Chenran (International Journal of Advanced AI Applications, 2026)
Honey pot contract operation code sequences exhibit strong concealment, significantly increasing detection complexity. To address this, this study proposes a fine-grained detection method based on LSTM and Fuzzing. By analyzing frequency differences across operation codes in different honey pot contract types, we calculate their occurrence rates and assign high initial weights to high-frequency operation codes. The weight mechanism is then integrated into the LSTM model to calculate operational code contribution levels and importance scores, enabling extraction of high-scoring critical operation codes. The research employs Fuzzing fuzz testing technology to generate initial test case sets and defines their deconstruction methods. Using case identifiers and functional codes, we validate interaction logic vulnerabilities in honey pot contracts through mutation factor probability matrices. By constructing source code graph structures using critical operation codes and interaction logic vulnerabilities, we update and aggregate vector nodes with global accumulation pooling functions to generate graph-level vectors. These graph-level vectors are then fed into graph attention networks, with cross-entropy loss functions jointly determining honey pot contract types. Test results demonstrate that the proposed method achieves sub-3 false positives for six honey pot contract types, demonstrating high precision in fine-grained detection.