Open Source Innovation Strengthens Cloudera’s Cybersecurity Solution and Accelerates Machine Learning at Scale

Open Source Innovation Strengthens Cloudera’s Cybersecurity Solution and Accelerates Machine Learning at Scale

Apache Spot (incubating), the community fighting cyber threats, releases Spot 1.0 as they progress towards Apache Software Foundation graduation

SINGAPORE – July 25, 2017 – Cloudera(NYSE: CLDR), the leading provider of the modern platform for machine learning and advanced analytics, announced the availability of Apache Spot 1.0 (incubating), which enables fast, easy, and more scalable cybersecurity machine learning. Spot is a community-driven cybersecurity project, built to bring advanced analytics to all IT Telemetry data on an open, scalable platform. Since Cloudera’s cybersecurity solution is built on Spot, this open source release strengthens the solution allowing enterprises to more effectively accelerate advanced threat detection at scale. Spot provides a community based approach to cybersecurity allowing organizations to collaborate across industries while simultaneously changing the economics of cybersecurity.

“Taking advantage of community based open source innovation and collaboration to strengthen our solutions and deliver customer value is at the heart of Cloudera’s strategy,” said Tom Reilly, chief executive officer at Cloudera. “With the release of Spot 1.0, we are excited to deliver a community-developed platform with which enterprises can protect themselves and collaborate with peer organizations in detecting cyber attacks in the hyper-connected world they operate in.”

Cloudera Plus Apache Spot Power Machine Learning Cybersecurity Applications
The Spot open source project delivers visibility into security threats by providing advanced threat detection using machine learning and advanced analytics. Spot is built on top of Cloudera’s platform leveraging Apache Spark and Hadoop, optimized for Intel hardware, and provides the ability to ingest and store high volumes of IT telemetry data for advanced threat detection with machine learning, accelerated threat investigation with complete contextual information at analyst finger tips, and a future-proofed open source infrastructure that changes the economics of cybersecurity.

Highlights from the Spot 1.0 release (incubating) include:
  • Improved machine learning performance with Spot’s upgrade to Apache Spark 2.1.
  • Better run times and model performance for all DNS, proxy, and NetFlow workloads due to improvements to the Suspicious Connects open source machine learning models.
  • Tighter integration with Cloudera’s platform to take advantage of Cloudera components while enhancing the Apache Spot open data model.

“It is difficult for cybersecurity teams across enterprises to collaborate when it comes to sharing threat intelligence and acting upon it. Spot is a platform designed to facilitate such collaboration starting with a shared common data model against which community developed machine learning algorithms can be run,” said Sam Heywood, director of Cybersecurity Strategy at Cloudera. “While threat intelligence feeds are extremely important in the fight against cyber criminals, we need to extend our analytics sharing capabilities to make sure we are detecting advanced behavioral anomalies. Apache Spot is an award winning technology community and project that is doing just that. Cloudera is playing its role by helping organizations take advantage of the open source project while making it easier to secure, manage, and scale community innovation in the cloud or on-premises.”

Cloudera’s cybersecurity solution, built on Apache Spot, uses advanced machine learning to baseline normal enterprise behavior across networks, endpoints, and users in order to see anomalies within the enterprise. Creating a single pane of glass for complete contextual security data allows for organizations to store multiple years worth of data at a lower cost while accelerating threat investigation and response. As cybersecurity threats become more mature and unique, organizations are in need of an open source approach to extend enterprise visibility while laying the foundation for advanced machine learning threat detection.

By joining the Apache Spot community, Cybraics, a leader in applying AI techniques to cybersecurity, can access and share information to help push in this line, advancements into the open source community and at the same time deliver results to their customers in less time,” said Alan Ross, CTO Cybraics and Apache Spot Founder. “As a contributor to Apache Spot, Cybraics will be sharing new analytics with the community.”

About Cloudera
Cloudera delivers the modern platform for machine learning and advanced analytics built on the latest open source technologies. The world’s leading organizations trust Cloudera to help solve their most challenging business problems by efficiently capturing, storing, processing and analyzing vast amounts of data. Learn more at cloudera.com.
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Cloudera, Hue and associated marks are trademarks or registered trademarks of Cloudera Inc. All other company and product names may be trademarks of their respective owners.
This press release contains forward-looking statements including, among other things, statements regarding the expected performance and benefits of Cloudera’s offerings. The words "believe," "may," "will," "plan," "expect," and similar expressions are intended to identify forward-looking statements. These forward-looking statements are subject to risks, uncertainties, and assumptions. If the risks materialize or assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. Risks include, but are not limited to, risks described in our filings with the Securities and Exchange Commission (SEC), including our Form S-1 Registration Statement, and our future reports that we may file with the SEC from time to time, which could cause actual results to vary from expectations. Cloudera assumes no obligation to, and does not currently intend to, update any such forward-looking statements after the date of this release.



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