Self-supervised Learning for Anomaly Detection in Computational Workflows
This addresses the problem of detecting anomalies in computational workflows for domains like cybersecurity and finance, but it is incremental as it builds on existing self-supervised and graph neural network techniques.
The paper tackles anomaly detection in computational workflows, which are modeled as graphs, by introducing an autoencoder-driven self-supervised learning approach that learns from unlabeled data to estimate normal behavior in a latent space, outperforming state-of-the-art methods on benchmark datasets.
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.