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Measuring similarity between IP addresses is an important task in the dailyoperations of any enterprise network. Applications that depend on an IPsimilarity measure include measuring correlation between security alerts,building baselines for behavioral modelling, debugging network failures andtracking persistent attacks. However, IPs do not have a natural similaritymeasure by definition. Deep Learning architectures are a promising solutionhere since they are able to learn numerical representations for IPs directlyfrom data, allowing various distance measures to be applied on the calculatedrepresentations. Current works have utilized Natural Language Processing (NLP)techniques for learning IP embeddings. However, these approaches have no properway to handle out-of-vocabulary (OOV) IPs not seen during training. In thispaper, we propose a novel approach for IP embedding using an adapted graphneural network (GNN) architecture. This approach has the advantages of workingon the raw data, scalability and, most importantly, induction, i.e. the abilityto measure similarity between previously unseen IPs. Using data from anenterprise network, our approach is able to identify similarities between localDNS servers and root DNS servers even though some of these machines are neverencountered during the training phase.

A Graph Neural Network Approach for Scalable and Dynamic IP Similarity in Enterprise Networks