Tag: Machine Learning Security

Splunk UBA uses machine learning to detect evolving threats beyond rule-based approaches in SOC operations, tackling overwhelming event volumes.

Splunk's Cui Lin explores fundamental techniques to validate data volume and monitor models to understand the size of your own UBA clusters.

This blog introduces new machine learning models in Splunk UBA for VPN connection monitoring to enhance WFH security resilience.

Use the Splunk Machine Learning Toolkit to predict the categorical value of any binary field in an event, and how this approach can be used to predict whether a financial trade will settle before its deadline based on the business semantics of related data.

This blog discusses in detail about detecting DNS data exfiltration attacks using deep learning

The Splunk Machine Learning for Security (SMLS) team introduces a new detection to detect DNS Tunneling using DNS TXT payloads.

Splunk's Kumar Sharad explains how to detect suspicious processes using recurrent neural networks.

The Splunk Machine Learning for Security team introduces a new detection to detect Domain Generation Algorithms generated domains.

The Splunk Threat Research Team shares a closer look at a hunting analytic and two machine learning-based detections that help find users running highly suspicious risky SPL commands.