基于梯度协同与特征融合的加密流量检测OA

With the widespread deployment of Internet of Things(IoT)devices and the rapid development of network communications,encrypted traffic has become the mainstream transmission form.However,it also provides covert channels for advanced threats such as backdoor attacks and targeted poisoning attacks.To address the critical security challenge of encrypted malicious traffic detection,this paper proposes an encrypted traffic detection model based on gradient collabora⁃tion and feature fusion networks,specifically designed to enhance the detection capability of encrypted malicious traffic in networks.The model consists of two core modules:the feature fusion module and the gradient collaboration module,which significantly improve the model's ability to learn representations of complex encrypted traffic patterns.In the feature fusion module,the model fully leverages the local feature extraction advantages of convolutional neural networks(CNN)and the global feature modeling capabilities of knowledge-augmented networks(KAN)to achieve efficient deep fusion of local and global features.To further enhance the collaboration and robustness among sub-models,the gradient collaboration mecha⁃nism enables multiple sub-models to dynamically share gradients in real-time and jointly optimize the loss function,thereby guiding and correcting each other during training,and strengthening the capture of diverse encrypted malicious traffic pat⁃terns.This mechanism not only alleviates conflicts between local and global feature learning but also significantly improves the model's sensitivity to covert encrypted attack traffic.Experimental results on multiple public encrypted traffic datasets show that the proposed model achieves an improvement of approximately 7％in F1 score compared to existing methods,en⁃abling high-precision classification of encrypted malicious traffic.