A Matrix Factorization Based Network Embedding Method for DNS Analysis

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.