Vector representation of internet domain names using a word embedding technique

Abstract

Word embeddings is a well known set of techniques widely used in natural language processing (NLP), and word2vec is a computationally-efficient predictive model to learn such embeddings. This paper explores the use of word embeddings in a new scenario. We create a vector representation of Internet Domain Names (DNS) by taking the core ideas from NLP techniques and applying them to real anonymized DNS log queries from a large Internet Service Provider (ISP). Our main objective is to find semantically similar domains only using information of DNS queries without any other previous knowledge about the content of those domains. We use the word2vec unsupervised learning algorithm with a Skip-Gram model to create the embeddings. And we validate the quality of our results by expert visual inspection of similarities, and by comparing them with a third party source, namely, similar sites service offered by Alexa Internet, Inc.