Batea: AI-based, context-driven network device ranking
A batea is a large shallow pan of wood or iron traditionally used by gold prospectors for washing sand and gravel to recover gold nuggets.
It is a context-driven network device ranking framework based on the anomaly detection family of machine learning algorithms. The goal of Batea is to allow security teams to automatically filter interesting network assets in large networks using nmap scan reports. We call those Gold Nuggets.
For more information about Gold Nuggeting and the science behind Batea, check out our whitepaper here
You can try Batea on your nmap scan data without downloading the software, using Batea Live here.
How it works
Batea works by constructing a numerical representation (numpy) of all devices from your nmap reports (XML) and then applying anomaly detection methods to uncover the gold nuggets. It is easily extendable by adding specific features, or interesting characteristics, to the numerical representation of the network elements.
The numerical representation of the network is constructed using features, which are inspired by the expertise of the security community. The features act as elements of intuition, and the unsupervised anomaly detection methods allow the context of the network asset, or the total description of the network, to be used as the central building block of the ranking algorithm. The exact algorithm used is Isolation Forest.
Machine learning models are the heart of Batea. Models are algorithms trained on the whole dataset and used to predict a score on the same (and other) data points (network devices). It also allows for model persistence. That is, you can re-use pretrained models and export models trained on large datasets for further use.
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