A Graph Neural Network Approach for Scalable and Dynamic IP Similarity in Enterprise Networks
This addresses a practical issue for enterprise network operators by enabling scalable and dynamic IP similarity measurement, though it is incremental as it builds on existing deep learning and graph neural network techniques.
The paper tackled the problem of measuring similarity between IP addresses in enterprise networks, which lacks a natural measure, by proposing a graph neural network approach that learns embeddings from raw data and handles out-of-vocabulary IPs, achieving the ability to identify similarities between unseen IPs like local and root DNS servers.
Measuring similarity between IP addresses is an important task in the daily operations of any enterprise network. Applications that depend on an IP similarity measure include measuring correlation between security alerts, building baselines for behavioral modelling, debugging network failures and tracking persistent attacks. However, IPs do not have a natural similarity measure by definition. Deep Learning architectures are a promising solution here since they are able to learn numerical representations for IPs directly from data, allowing various distance measures to be applied on the calculated representations. Current works have utilized Natural Language Processing (NLP) techniques for learning IP embeddings. However, these approaches have no proper way to handle out-of-vocabulary (OOV) IPs not seen during training. In this paper, we propose a novel approach for IP embedding using an adapted graph neural network (GNN) architecture. This approach has the advantages of working on the raw data, scalability and, most importantly, induction, i.e. the ability to measure similarity between previously unseen IPs. Using data from an enterprise network, our approach is able to identify similarities between local DNS servers and root DNS servers even though some of these machines are never encountered during the training phase.