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VAST Data Simplifies And Secures Hybrid Cloud Data Management While Delivering Up To 80 Per Cent Cost Savings On Cloud

The VAST Data Platform Version 5.0 Adds New AI-Based Event Detection, Real-Time Monitoring and Simple Provisioning Tools for AWS Hybrid Cloud Deployments ....

David Feng , Jeff Denworth , Allen Institute , Amazon Web Services , Cent Cost Savings On Cloudthursday , Scientific Computing , Protocol Namespace , Cluster Manager , Anomaly Detection , Time Audit Log Analytics , Security Event , Incident Management , Vast Data , Omputing And Internet ,

iTWire - VAST Data Simplifies and Secures Hybrid Cloud Data Management While Delivering up to 80 Per Cent Cost Savings on AWS Cloud

COMPANY ANNOUNCEMENT:  The VAST Data Platform Version 5.0 Adds New AI-Based Event Detection, Real-Time Monitoring and Simple Provisioning Tools f. ....

David Feng , Jeff Denworth , Amazon Web Services , Allen Institute , Scientific Computing , Protocol Namespace , Cluster Manager , Anomaly Detection , Time Audit Log Analytics , Security Event , Incident Management , Artifical Intelligence Ai , Data Platform , Vast Data ,

Business.Scoop » VAST Data Simplifies And Secures Hybrid Cloud Data Management While Delivering Up To 80 Per Cent Cost Savings On Cloud

Business Scoop provides news and views on Business and Markets from New Zealand ....

David Feng , Jeff Denworth , Amazon Web Services , Allen Institute , Press Release , Platform Version , Based Event Detection , Real Time Monitoring , Simple Provisioning Tools , Scientific Computing , Protocol Namespace , Cluster Manager , Anomaly Detection , Time Audit Log Analytics , Security Event , Incident Management , New Zealand ,

"Anomaly Detection in Online Data Streams Using Deep Belief Neural Netw" by Dharani Kumar Talapula, Adarsh Kumar et al.

Internet technologies are now utilized in almost every domain to gather data streams and also to monitor important events in an organization. However, these data streams are affected by abnormal or unusual pattern widely known as anomalies, which is responsible for malicious attacks, hardware failure, software failure, and reading errors. Hence, a dynamic, effective anomaly detection model is established in this research to enhance the quality of data gathered by the networks. The deep belief neural network is employed to obtain promising results in anomaly detection. The classifier performance is improved by reducing data dimensionality through the process known as feature extraction. The experimental analysis demonstrates the effectiveness of the proposed methodology by comparing it with existing techniques such as RNN, BNN, CNN, and Autoencoders. The experimental outcome such as accuracy, precision, F1-score, and error of about 80.5714%, 94.5440%, and95.3% for the HDFS dataset revea ....

Anomaly Detection , Data Streaming , Eep Belief Nn , Deep Learning ,

Stream Security Expands into CloudSecOps Market with Launch of Real-time Cloud Security Solution

/PRNewswire/ Stream Security, previously known as Lightlytics, today announced a significant expansion into cloud security, ushering in a new era for the. ....

Kobi Samboursky , Stream Security Co , Stream Security , Cloud Twin , Stream Security Co Founder , Anomaly Detection ,