Blockchain-Based Distributed Network Security Architecture with Smart Contract Vulnerability Detection Using Improved Tree CNN

In the era of big data, information security and privacy protection have become important issues facing today's society. This study proposes a distributed network security architecture based on blockchain to enhance the security of information privacy protection. The proposed architecture consists of three primary levels: equipment layer, network service layer, and application layer. It also integrates smart contracts. In addition, this study also proposes a vulnerability detection method based on improved tree convolutional neural networks. The incorporation of a "continuous binary tree" approach effectively addresses the limitation inherent to conventional tree convolution, wherein the number of nodes is fixed. This refinement enables a more effective capture of the hierarchical structure and semantic nuances inherent to smart contract code. The experiment used multiple datasets, each containing multiple IoT attack types and smart contract vulnerability code snippets. These datasets were evaluated based on a set of criteria, including but not limited to accuracy, recall, F1 scores, gas costs, and execution delays. Experiments have shown that the proposed method performs well in accuracy, precision, recall, and F1 scores compared to existing state-of-the-art methods, with an accuracy range of 89.62% to 98.36%, significantly better than Oyente (about 75%) and Securify (about 85%). Specifically, the proposed method achieved 96.14% accuracy in detecting reentrant attacks, compared to 78% for Oyente and 82% for Securify. The findings indicate that the architectural design exerts a substantial influence on enhancing network security performance, thereby ensuring the stability of the system by effectively mitigating the variability in response time.