置信度引导的跨模态知识迁移用于微服务系统中的多模态异常检测

中文摘要 AI 辅助

准确的异常检测对于微服务系统的可靠和安全运行至关重要。尽管越来越多的研究已从单模态建模转向多模态交互与融合，但有效利用可靠的跨模态信息仍然具有挑战性。这一挑战主要源于两个方面。首先，不同模态受到负载波动等因素的影响，导致其可靠性动态变化。其次，多模态数据在结构和语义上均存在异质性。因此，我们提出了一种置信度引导的跨模态知识迁移方法用于多模态异常检测（CMT-AD）。该方法在统一的深度聚类框架内联合建模指标和日志，并通过软聚类分布估计模态可靠性，其中聚类不确定性被量化为置信度分数。在这些置信度分数的引导下，模型主动分析各模态在跨模态交互中的贡献，并用高置信度模态的知识补充低置信度模态。为进一步缓解跨模态异质性，我们引入了一个门控中间模态，并设计了结构和语义一致性约束，将原始模态与中间模态对齐，以保留跨模态的相似性结构和语义分布。此外，还加入了模态内和跨模态正则化项，以增强聚类紧凑性并减轻负迁移。我们在三个大规模数据集上评估了CMT-AD，结果表明CMT-AD优于最先进的方法，并取得了高于0.9的F1分数。

英文摘要

Accurate anomaly detection is essential for reliable and secure operations of microservice systems. While an increasing number of studies have shifted from unimodal modeling to multimodal interaction and fusion, effectively leveraging reliable cross-modal information remains challenging. The challenge primarily stems from two aspects. Firstly, different modalities are influenced by factors like load fluctuations, leading to dynamically changing reliability. Secondly, multimodal data exhibit heterogeneity in both structure and semantics. Therefore, we propose a confidence-guided Cross-Modal knowledge Transfer method for multimodal Anomaly Detection (CMT-AD). It jointly models metrics and logs within a unified deep clustering framework and estimates modality reliability through the soft clustering distributions, where clustering uncertainty is quantified into confidence scores. Guided by these confidence scores, the model actively analyzes the contributions of each modality in cross-modal interactions and supplements low-confidence modalities with knowledge from high-confidence ones. To further mitigate cross-modal heterogeneity, we introduce a gated intermediate modality and design structural and semantic consistency constraints that align the original modalities with the intermediate modality to preserve similarity structures and semantic distributions across modalities. Furthermore, intra-modal and cross-modal regularization terms are incorporated to enhance cluster compactness and mitigate negative transfer. We evaluated CMT-AD on three large-scale datasets, and the results demonstrate that CMT-AD outperforms state-of-the-art approaches and achieves an F1-score higher than 0.9.

发表机构

• Dalian Maritime University（大连海事大学）
• Queen’s University Belfast（贝尔法斯特女王大学）

机构由 AI 辅助整理，请以论文原文为准。