Argus provides a rich source of transactional network data that is suitable for many machine learning algorithms. Whether you are interested in credit card fraud approaches to cyber security, or pattern recognition to identify user network use behavior, Argus enables a platform that can support real-time machine learning classification and identification, as well as supervised, unsupervised, semi-supervised learning, regression, instanced-based, regularization, decision tree, clustering, association rule learning, dimensional reduction, or ensemble analytics.
So many because Argus doesn't limit how you analyze the data and the type of learning that you want to do.
Argus supports TensorFlow and Keras learning engines through Python. Python is a great platform for processing Argus data. Use Argus tools to generate general or specific network transactional data, collect, store, process, reduce, aggregate and extract the features needed for your ML analytics, and use Python and its libraries and functions, to analyze and visualize the training and the result.
These posts will describe in parts the steps needed to build an Argus ML workflow, reading data from Argus, inputing into a Python Panda’s dataframe, and then performing some basic exploratory data analysis.
