Work

Malicious encrypted traffic detection method for service chains in multi-cloud environments

Year: 2026

Type: article

Abstract: To resolve the pain points of insufficient feature characterization, weak cross-domain generalization and data privacy constraints in malicious encrypted traffic detection for multi-cloud service chains, this paper proposes a hierarchical multimodal detection framework (HMM-CSCD) tailored for encrypted traffic in multi-cloud service chain environments. The framework introduces three complementary feature modalities and employs federated self-supervised learning to learn generic traffic representations without exposing raw data across cloud domains. A dynamic attention-based fusion mechanism further enables real-time adaptation of modality weights according to instantaneous transmission states. Experiments conducted on the cloud platform of the China Electric Power Research Institute demonstrate that HMM-CSCD achieves consistent improvements in detection accuracy and cross-cloud generalization while maintaining an efficient single-flow inference latency of 2.32 ms. The proposed model offers a practical and privacy-preserving solution for securing network traffic in distributed cloud service chains.

Source: Journal of Cloud Computing Advances Systems and Applications

DOI: 10.1186/s13677-026-00966-7

Authors: Yuncheng Xie, Kehe Wu, Yuan Jiang

Institutions: North China Electric Power University, China Electric Power Research Institute

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,790