Dns2Vec: Exploring Internet Domain Names through Deep Learning

The concept of vector space embeddings was first applied in the area of Natural Language Processing (NLP) but has since been applied to several domains wherever there is an aspect of semantic similarity. Here we apply vector space embeddings to Internet Domain Names. We call this Dns2Vec. A corpus of Domain Name Server (DNS) queries was created from traffic from a large Internet Service Provider (ISP). A skipgram word2vec model was used to create embeddings for domain names. The objective was to find similar domains and examine if domains in the same category (news, shopping etc.) cluster together. The embeddings could then be used for several traffic engineering application such as shaping, content filtering, prioritization and also for predicting browsing sequence and anomaly detection. The results were confirmed by manually examining similar domains returned by the model, visualizing clusters using t-SNE and also using a 3rd party web categorization service (Symantec K9).