2022/12/16 by James Pope, Jinyuan Liang, Pope, James +29
Computer Science · Decision Sciences · #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #FOS: Electrical engineering #Network Security and Intrusion Detection #Software System Performance and Reliability #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.08525
openalex publication_date 2022/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond what is necessary for many security tasks and are not feasible for resource constrained environments, such as edge devices. To address this problem, we present the resource-interaction graph that is built directly from the audit log. We show that the resource-interaction graph's storage requirements are significantly lower than provenance graphs using an open-source data set with two container escape attacks captured from an edge device. We use a graph autoencoder and graph clustering technique to evaluate the representation for an anomaly detection task. Both approaches are unsupervised and are thus suitable for detecting zero-day attacks. The approaches can achieve f1 scores typically over 80% and in some cases over 90% for the selected data set and attacks.