Remote Management Device Identification Based on Multimodal Feature Fusion

To achieve high-performance identification for remote management device, this paper proposes MME4RMD, a novel BERT-ResNet-based multi-modal embedding model that integrates visual, textual, and HTML structural features to generate distinctive device embedding. By employing triplet network training, our method significantly enhances feature discrimination and generalization capability for cluster-based identification. Comprehensive evaluations on manually curated real-world datasets containing 25 known and 20 unknown device classes demonstrate superior performance, achieving 98% and 93% F1-scores for known and unknown device identification respectively, substantially outperforming conventional approaches.