Posts Tagged ‘Prelert’

Prelert’s Elasticsearch Equipped with Anomaly Detection

Prelert logoDaniel Gutierrez reported, “Prelert, the anomaly detection company, today announced the release of an Elasticsearch Connector to help developers quickly and easily deploy its machine learning-based Anomaly Detective® engine on their Elasticsearch ELK (Elasticsearch, Logstash, Kibana) stack. Earlier this year, Prelert released its Engine API enabling developers and power users to leverage its advanced analytics algorithms in their operations monitoring and security architectures. By offering an Elasticsearch Connector, the company further strengthens its commitment to democratizing the use of machine learning technology, providing tools that make it even easier to identify threats and opportunities hidden within massive data sets. Written in Python, the Prelert Elasticsearch Connector source is available on GitHub. This enables developers to apply Prelert’s advanced, machine learning-based analytics to fit the big data needs within their unique environment.”

The article continues with, “Prelert’s Anomaly Detective processes huge volumes of streaming data, automatically learns normal behavior patterns represented by the data and identifies and cross-correlates any anomalies. It routinely processes millions of data points in real-time and identifies performance, security and operational anomalies so they can be acted on before they impact business. The Elasticsearch Connector is the first connector to be officially released by Prelert. Additional connectors to several of the most popular technologies used with big data will be released throughout the coming months.”

Read more here.

Image courtesy Prelert.

Machine Learning Predictive Analytics Take On Hacks, APM And More

rsz_prelertpixLast week the world learned that the hacks at Target hit more customers than originally thought – somewhere in the 100 million vicinity – and that Neiman Marcus also saw customer credit card information spirited away by data thieves. They’re not the first big-name outfits to suffer a security setback, could they be the last?

No one can ever say never, of course. But it’s possible that new tools that leverage machine learning predictive analytics could put a serious dent in the black hats’ handiwork, while also improving IT’s hand at application performance management.

A big problem in both the APM and security space today is that there’s just a ton of data coming at IT pros dealing with those issues, much of it just describing the normal state of affairs, and no one’s got time to spend reviewing that. What IT staffers want to know about are problems, which leads to a lot of rules-writing to identify thresholds that could point to issues, and to a lot of rewriting of those rules to account for the fact that things change fast in today’s world of system complexity – and to a lot of misses because of the impossibility of keeping up. Sixty percent of problems are still reported by users, not the tools IT is using, says Kevin Conklin, marketing vp at Prelert, whose machine learning predictive analytics technology is used in CA’s Application Behavior Analytics and available as Anomaly Detective for the Splunk IT apps ecosystem.

Read more