论文标题
使用泊松矩阵分解中计算机网络中的图形链接预测
Graph link prediction in computer networks using Poisson matrix factorisation
论文作者
论文摘要
Graph Link预测是网络安全中的一项重要任务：计算机网络中实体之间的关系，例如用户与计算机交互或系统库以及使用它们的相应过程，可以为对抗行为提供关键的见解。泊松矩阵分解（PMF）是大型网络中链路预测的流行模型，对于其可扩展性尤其有用。在本文中，PMF的扩展为包括在网络安全应用中通常遇到的方案。具体而言，提出了一个扩展，以明确处理二进制邻接矩阵，并包括与图节点相关的已知分类协变量。还提出了季节性PMF模型来处理季节性网络。为了允许这些方法扩展到大图，讨论了用于执行快速推断的变异方法。结果表明，与标准PMF模型和其他统计网络模型相比，性能的改善。
Graph link prediction is an important task in cyber-security: 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 cyber-security 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.
