In this paper, I explore the potential of network embedding (a.k.a. graph representation learning) to characterize DNS entities in passive network traffic logs. I propose an MF-DNS-E (\underline{M}atrix-\underline{F}actorization-based \underline{DNS} \underline{E}mbedding) method to represent DNS entities (e.g., domain names and IP addresses), where a random-walk-based matrix factorization objective is applied to learn the corresponding low-dimensional embeddings.

翻译：本文探讨了网络嵌入（亦称图表示学习）在刻画被动网络流量日志中DNS实体方面的潜力。我提出了一种MF-DNS-E（基于矩阵分解的DNS嵌入）方法来表示DNS实体（例如域名和IP地址），该方法采用基于随机游走的矩阵分解目标来学习相应的低维嵌入。