Anomaly detection is the task of identifying abnormal behavior of a system.
Anomaly detection in computational workflows is of special interest because of
its wide implications in various domains such as cybersecurity, finance, and
social networks. However, anomaly detection in computational workflows~(often
modeled as graphs) is a relatively unexplored problem and poses distinct
challenges. For instance, when anomaly detection is performed on graph data,
the complex interdependency of nodes and edges, the heterogeneity of node
attributes, and edge types must be accounted for. Although the use of graph
neural networks can help capture complex inter-dependencies, the scarcity of
labeled anomalous examples from workflow executions is still a significant
challenge. To address this problem, we introduce an autoencoder-driven
self-supervised learning~(SSL) approach that learns a summary statistic from
unlabeled workflow data and estimates the normal behavior of the computational
workflow in the latent space. In this approach, we combine generative and
contrastive learning objectives to detect outliers in the summary statistics.
We demonstrate that by estimating the distribution of normal behavior in the
latent space, we can outperform state-of-the-art anomaly detection methods on
our benchmark datasets.

我们提出了一种基于自编码器驱动的无监督学习方法，从无标签的工作流数据中学习摘要统计，并估计计算工作流的正常行为。通过估计潜在空间中正常行为的分布，我们证明了我们可以在基准数据集上胜过最先进的异常检测方法。