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Detecting rare and unusual processes with Elastic machine learning

In SecOps, knowing which host processes are normally executed and which are rarely seen helps cut through the noise to quickly locate potential problems or security threats. By focusing attention on rare anomalies, security teams can be more efficient when trying to detect or hunt for potential threats. Finding a process that doesn’t often run on a server can sometimes indicate innocuous activity or could be an indication of something more alarming.

AWS Machine Learning Tools (2021 edition)

When you want to stay ahead and on top of things in a fast-moving industry, machine learning (ML) is surely one of the trending solutions. Today, innovative companies already have leading Machine Learning tools well-integrated into their processes. In comparison, your start could seem dreadfully slow. Or maybe you just don’t have the time or resources to invest in running your own Machine Learning training infrastructure.

Detecting threats in AWS Cloudtrail logs using machine learning

Cloud API logs are a significant blind spot for many organizations and often factor into large-scale, publicly announced data breaches. They pose several challenges to security teams: For all of these reasons, cloud API logs are resistant to conventional threat detection and hunting techniques.

The Road to Zero Touch Goes Through Machine Learning

The telecom industry is in the midst of a massive shift to new service offerings enabled by 5G and edge computing technologies. With this digital transformation, networks and network services are becoming increasingly complex: RAN, Core and Transport are only a few of the network’s many layers and integrated components. Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks.

Using Elastic machine learning rare analysis to hunt for the unusual

It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.

AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few models that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.

Threat Hunting With ML: Another Reason to SMLE

Security is an essential part of any modern IT foundation, whether in smaller shops or at enterprise-scale. It used to be sufficient to implement rules-based software to defend against malicious actors, but those malicious actors are not standing still. Just as every aspect of IT has become more sophisticated, attackers have continued to innovate as well. Building more and more rules-based software to detect security events means you are always one step behind in an unsustainable fight.

Creating a Fraud Risk Scoring Model Leveraging Data Pipelines and Machine Learning with Splunk

According to the Association of Certified Fraud Examiners, the money lost by businesses to fraudsters amounts to over $3.5 trillion each year. The ACFE's 2016 Report to the Nations on Occupational Fraud and Abuse states that proactive data monitoring and analysis is among the most effective anti-fraud controls.

Levelling up your ITSI Deployment using Machine Learning

Here at Splunk we’re passionate about helping our customers get as much value from their data as possible. Recently Lila Fridley has written about how to select the best workflow for applying machine learning and Vinay Sridhar has provided an example of anomaly detection in SMLE.