基于智能合约漏洞检测的元模型算法
Metamodel Algorithm Based on Smart Contract Vulnerability Detection

本文提出了一种基于元学习的智能合约漏洞检测方法，通过结合多层感知机模型和MetaSGD优化器，实现了高效的训练和检测性能。该方法能够在少量样本和有限梯度更新的条件下，快速适应新任务，并取得良好的泛化效果。同时，神经网络策略的引入进一步加速了梯度强化学习的微调过程。实验结果表明，该方法在智能合约漏洞检测任务中具有较强的实用性和可靠性，为智能合约安全提供了一种高效的解决方案。
This paper proposes a meta-learning-based smart contract vulnerability detection method, which achieves efficient training and detection performance by combining a multi-layer perceptron model and a MetaSGD optimizer. This method can quickly adapt to new tasks and achieve good generalization effects under the conditions of a small number of samples and limited gradient updates. At the same time, the introduction of the neural network strategy further accelerates the fine-tuning process of gradient reinforcement learning. Experimental results show that this method has strong practicality and reliability in smart contract vulnerability detection tasks, and provides an efficient solution for smart contract security.