信息安全研究2025，Vol.11Issue(5)：457-464,8.DOI:10.12379/j.issn.2096-1057.2025.05.08
工业互联网中融入域适应的混合神经网络加密恶意流量检测
Hybrid Neural Network Encrypted Malicious Traffic Detection in the Industrial Internet with Domain Adaptation
摘要
Abstract
With the rapid development of information technology in the field of industrial control,the industrial Internet has become a major target for cyberattacks,making malicious traffic detection increasingly important.However,the widespread use of encryption allows attackers to easily hide malicious communication content,rendering traditional content-based detection methods ineffective.This paper proposes an encrypted malicious traffic detection scheme based on a hybrid neural network and domain adaptation.The scheme integrates ResNet,ResNext,DenseNet,and similarity detection algorithms to construct a hybrid neural network.On this basis,a domain adaptation module is added to reduce data bias.By preprocessing streams from a public industrial Internet dataset,deep features are extracted from encrypted traffic without decryption.The hybrid neural network outputs higher-dimensional feature vectors that leverage the strengths of each model.A domain classifier within the domain adaptation module enhances the model's stability and generalization across different network environments and time periods,enabling accurate classification of malicious traffic.Experimental results show that the proposed scheme improves accuracy and efficiency in detecting encrypted malicious traffic.关键词
工业互联网/混合神经网络/加密恶意流量/相似度检测/域适应Key words
industrial Internet/hybrid neural network/encrypted malicious traffic/similarity detection/domain adaptation分类
信息技术与安全科学引用本文复制引用
张浩和,韩刚,杨甜甜,黄睿..工业互联网中融入域适应的混合神经网络加密恶意流量检测[J].信息安全研究,2025,11(5):457-464,8.基金项目
国家自然科学基金项目(62102312) （62102312）
陕西省重点研发计划项目(2024GX-YBXM-079) （2024GX-YBXM-079）
ISN全国重点实验室开放课题(ISN24-13) （ISN24-13）
陕西省科协青年人才托举计划项目(20210119) （20210119）
陕西省高校青年创新团队项目(23JP162) （23JP162）
