While Web3.0 technology promotes the development of distributed application ecology, binary executable file security threats brought by its open architecture become increasingly prominent. Traditional detection methods are difficult to deal with attack means such as code confusion and control flow camouflage due to their dependence on static feature matching. To this end, we propose a hybrid detection model combining depth map structure analysis and sequence feature mining. The model constructs complementary representation by synchronously analyzing the topological features of the control flow graph and the microscopic operation mode of the instruction sequence. The cyclic structure extraction method is introduced to accurately locate the strongly coupled execution path, and the attention mechanism is used to strengthen the recognition of covert attack patterns. At the same time, a dynamic adversarial training framework is constructed to simulate the code deformation strategy in real attack scenarios to learn the invariance characteristics of attack. Experiments show that the detection accuracy of the model is 3.8 percentage points higher than that of the traditional graph neural network in real data sets, the recognition rate of countersample is 90.6%, and the feature disturbance test shows that the decision stability is increased by 41.2%. The research results can not only effectively detect Web3.0 binary malicious code, but also be applied to blockchain security scenarios such as smart contract audit. It lays a foundation for exploring the dynamic and static feature fusion mechanism to deepen polymorphic attack defense.

Hongjun Wu. Optimization of Malicious Code Detection Model for Web3.0 Binary Files Based on Convolutional Neural Network and Graph Convolutional Neural Network. International Journal of Multimedia Computing (2025), Vol. 6, Issue 1: 29-43. https://doi.org/10.38007/IJMC.2025.060103.

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