Platform Blogs

This blog is the first in a mini-series of blogs where we aim to explore and share various aspects of our security team’s mindset and learnings. In this post, we will introduce you to how our own security and threat research team develops the latest security detections using ML.

One of the new necessities we came across several times was that the clients were willing to get a sport bets fraud risk scoring model to be able to quickly detect fraud. For that purpose, I designed a data pipeline to create a sport bets fraud risk scoring model based on anomaly detection algorithms built with Probability Density Function powered by Splunk’s Machine Learning Toolkit.

To help our customers extract the most value from their IT Service Intelligence (ITSI) deployments, Splunker Greg Ainslie-Malik created this blog series. Here he presents a number of techniques that have been used to get the most out of ITSI using machine learning.

How can you use statistical analysis to identify whether you have an unusual number of events, and how can similar techniques be applied to non-numeric data to see if descriptions and sourcetype combinations appear unusual? Read all about it in this blog.

Root cause analysis can be a difficult challenge when you are troubleshooting complex IT systems. In this blog, we are going to take you through how you can perform root cause analysis on your IT Service Intelligence (ITSI) episodes using machine learning, or more specifically causal inference.

In this blog we are going to describe how you can create a notable event policy in IT Service Intelligence (ITSI) that is able to group your events using labels generated by unsupervised machine learning in the Smart ITSI Insights App for Splunk – and don’t worry you don’t have to be a data scientist to read this blog!

As we are trying to commoditize machine learning through our MLTK smart workflows, this article outlines another example of an MLTK smart workflow, designed to help improve the usability of the predictive capabilities in ITSI.

In this blog post, we’ll explore an ML-powered solution using the Splunk Machine Learning Environment to detect fraudulent credit card transactions in real time. Using out-of-the-box Splunk capabilities, we’ll walk you through how to ingest and transform log data, train a predictive model using open source algorithms, and predict fraud in real-time against transaction events.

Check out how the Splunk AR web app allows administrators to manage their entire AR experience at scale and all in one unified place.