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Devo Time Series Forecasting
Devo’s Time Series Forecasting capabilities allow you to analyze any time-series data set and predict the future value of a metric. Learn more at http://www.devo.com
Devo Single-metric Time Series Anomaly Detection
Devo's Machine Learning capabilities can be used to monitor and model time series metrics to accurately identify, score, and alert on anomalies. Learn more at http://www.devo.com
Devo Machine Learning - Quick Modeler K-Means Clustering Example
The Quick Modeler just one component of the ML Workbench, a suite of tools designed to bring Machine Learning and Data Science Solutions to Life on the Devo Platform. Learn more at http://www.devo.com
Devo Multi-metric Time Series Anomaly Detection
Multi-metric Time Series Anomaly Detection is a powerful Devo capability that enables IT operators to understand and gain a broad view of their ecosystem at a glance by leveraging ML. Learn more at http://www.devo.com
Teramind UAM product overview: Employee monitoring and User Entity Behavior Analytics (UEBA)
Teramind UAM provides a comprehensive employee monitoring platform, providing user activity monitoring (UAM), user entity behavior analytics (UEBA), audit and forensics capabilities, while accommodating for data privacy requirements.
Logsentinel PAM Demo: Privileged Access Management and Event Logging
LogSentinel #PAM Protects From Log Tampering There's a significant risk for a privileged Linux user to tamper with company data and try to avoid being detected by clearing logs. Such log tampering may potentially threaten one’s business continuity. That’s why we developed LogSentinel PAM, which can be implemented in just a few steps.
EventSentry v4.0.3 Installation Walk-Through
EventSentry v4.0 installation from start to finish.
Server Hardening Sec-Ops Gap
Security versus production functionality is always an issue that put stress between security teams and IT Ops. Learn how CalCom CHS can solve this conflict, and keep all sides satisfied.
The Netacea Approach | Smarter Bot Management Powered by Machine Learning
The majority of internet traffic is now made up of bots. Many bots are malicious, and actively looking for the next opportunity to attack. In fact, bots make 90% of all login attempts. They also pretend to be human, trying to bypass security measures and evade detection by mimicking human behaviour. Worse, the old defences aren’t enough on their own. Manual analysis, rules-based defences and web application firewalls just can’t keep pace with the ferocity of these attacks.