基于深度学习和区块链的JavaScript恶意代码检测系统

目前基于深度学习的恶意代码检测技术是恶意代码检测领域的研究热点,然而大多数研究集中于如何改进算法来提高恶意代码检测的准确率,忽略了恶意代码数据集样本标签的不足导致无法训练出高质量的模型.本文利用区块链技术来解决恶意代码检测数据样本孤岛和数据可信任的问题;同时在代码特征提取上,使用马尔可夫图算法提取特征;基于分布式深度学习的训练融合区块链去中心化,可溯源不可篡改的优势,将不同算力贡献者采用同步训练更新模型参数.通过仿真实验和理论分析验证了该方法的可行性和巨大的潜力.

JavaScript Malicious Code Detection System Based on Deep Learning and Blockchain

At the moment, the detection technology of malicious code based on deep learning is a research hotspot in the field of malicious code detection. However, most researches focus on how to improve the algorithm to enhance the detection accuracy of malicious code, but ignore the lack of sample tags in the data set of malicious code, failling to train high-quality models. In this study, the problem of detecting isolated islands of data samples and data trustworthiness of malicious code is solved by Blockchain technology, and code features are extracted with the Markov graph algorithm. The training fusion block chain based on distributed deep learning has the advantages of decentralization, traceability and non-tampering, and the contributors of different computing power adopt synchronous training to update model parameters. The feasibility and great potential of this method are verified by simulation experiments and theoretical analysis.