Using the Elastic Stack for Security ML use cases

United States and Canada Virtual
Wed, Jan 19, 9:00 AM (MST)

57 RSVP'ed

About this event

The Elastic Stack is the perfect one-stop shop for production-level machine learning. As a search company, Elastic is built to efficiently handle large amounts of data. Searching and aggregating data for analysis is made simple and intuitive using Elasticsearch Query DSL. Through personalized dashboards, various chart display options, and aggregation capabilities, you can visualize large data sets in Kibana. It also supports both supervised and unsupervised machine learning model training, which includes Classification, Regression, and Anomaly Detection models. Furthermore, Painless, the Stack’s personalized scripting language, can be used to write your own custom models.

The Security Data Science team has been using capabilities in the Elastic Stack to enhance the experience of our Elastic Security Users, by providing better guidance via entity analytics, environmental contexts, and recommended actions in the Security App. In this presentation, we will talk about existing and upcoming capabilities in the Elastic Stack that can be leveraged for machine learning and data science. We will also dive into case studies highlighting how we have been and using them internally to solve Security use cases.