GRAPH LINK PREDICTION IN COMPUTER NETWORKS USING POISSON MATRIX FACTORISATION

• 成果类型: Article
• 署名作者: Passino, Francesco Sanna; Turcotte, Melissa J. M.; Heard, Nicholas A.
• 署名单位: Imperial College London; Microsoft
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1540
• 发表日期: 2022
• 页码: 1313-1332
• 关键词: models; inference

摘要

Graph link prediction is an important task in cybersecurity: relationships between entities within a computer network, such as users interacting with computers or system libraries and the corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article PMF is extended to include scenarios that are commonly encountered in cybersecurity applications. Specifically, an extension is proposed to explicitly handle binary adjacency matrices and include known categorical covariates associated with the graph nodes. A seasonal PMF model is also presented to handle seasonal networks. To allow the methods to scale to large graphs, variational methods are discussed for performing fast inference. The results show an improved performance over the standard PMF model and other statistical network models.