| 摘要: |
| 根因分析是指找出引起复杂系统异常故障的根源因素. 基于因果关系的溯因方法基于结构因果模型, 是实现根因分析的最优选择之一. 目前大多数因果驱动的根因分析方法大都需要数据因果结构的发现作为前置条件, 这使得根因分析本身严重依赖于因果发现这一先验任务的效果. 最近, 基于得分函数的干预识别受到了广泛关注, 其通过对比干预前后的得分函数导数的方差来检测被干预的变量集合, 具备突破因果发现对根因分析约束的潜力. 然而, 主流的基于得分函数的干预识别大都受限于得分函数估计这一步骤, 其采用的解析求解方法并不能很好地对真实的高维复杂数据分布进行建模. 因此, 鉴于最近在数据生成中取得的进展, 提出一种扩散模型引导的根因分析策略. 具体来说, 所提方法首先利用扩散模型针对异常发生前后的数据分布对应的得分函数进行估计, 进而通过观察对加权融合后的总体得分函数的一阶导方差, 识别导致异常发生的根因变量集合. 此外, 为了进一步减小在识别过程中剪枝操作带来的扩散模型重复训练的开销, 提出一种可靠的估计策略, 其只需要训练一次扩散模型即可估计所有剪枝过程中对应节点的得分函数. 在仿真数据和真实数据上的实验结果表明, 所提出的方法实现了对于根因变量集合的精准识别. 此外, 相关的消融实验也表明, 扩散模型的引导作用对于表现提升至关重要. |
| 关键词: 根因分析 扩散模型 结构因果模型 |
| DOI：10.13328/j.cnki.jos.007473 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(62032024, 62372459, 62276273, 62302503); 国防科技大学青年自主创新科学基金(ZK23-15); 高性能计算国家重点实验室自主开放课题(202401-09) |
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| Diffusion-model-guided Root Cause Analysis |
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WANG Hao-Tian1, ZHOU Xue-Guang2, WANG Shang-Wen1, JIN Ruo-Chun1, HUANG Wan-Rong1, YANG Wen-Jing1, WANG Ji1
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1.College of Computer Science, National University of Defense Technology, Changsha 410073, China;2.Department of Information Security, Naval University of Engineering, Wuhan 430033, China
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| Abstract: |
| Root cause analysis refers to identifying the underlying factors that lead to abnormal failures in complex systems. Causal-based backward reasoning methods, founded on structural causal models, are among the optimal approaches for implementing root cause analysis. Most current causality-driven root cause analysis methods require the prior discovery of the causal structure from data as a prerequisite, making the effectiveness of the analysis heavily dependent on the success of this causal discovery task. Recently, score function-based intervention identification has gained significant attention. By comparing the variance of score function derivatives before and after interventions, this approach detects the set of intervened variables, showing potential to overcome the constraints of causal discovery in root cause analysis. However, mainstream score function-based intervention identification is often limited by the score function estimation step. The analytical solutions used in existing methods struggle to effectively model the real distribution of high-dimensional complex data. In light of recent advances in data generation, this study proposes a diffusion model-guided root cause analysis strategy. Specifically, the proposed method first estimates the score functions corresponding to data distributions before and after the anomaly using diffusion models. It then identifies the set of root cause variables by observing the variance of the first-order derivatives of the overall score function after weighted fusion. Furthermore, to solve the issue of computational overhead raised by the pruning operation, an acceleration strategy is proposed to estimate the score function from the initially trained diffusion model, avoiding the re-training cost of the diffusion model after each pruning operation. Experimental results on simulated and real-world datasets demonstrate that the proposed method accurately identifies the set of root cause variables. Furthermore, ablation studies show that the guidance provided by the diffusion model is critical to the improved performance. |
| Key words: root cause analysis (RCA) diffusion model structural causal model (SCM) |
