User & Entity Behavior Analytics (UEBA)

Machine Learning-empowered, automated security platform

Adlumin provides a cloud-native streaming analytics platform designed to discover threats, malfunctions, and IT operations failures across any log data stream. Data from individual users and entities such as servers, workstations, and endpoints can be ingested into the application for baselining expected behavior. Deviations from such baselines can signal cyberattacks and other events requiring attention.

Machine Learning-empowered, automated security platform

Adlumin provides a cloud-native streaming analytics platform designed to discover threats, malfunctions, and IT operations failures across any log data stream. Data from individual users and entities such as servers, workstations, and endpoints can be ingested into the application for baselining expected behavior. Deviations from such baselines can signal cyberattacks and other events requiring attention.

Adlumin’s UEBA Features

 No Data Limits

Adlumin delivers hyper-scalable patented architecture for ingesting streaming data and continuously training. The platform updates machine learning algorithms for anomaly detection across an infinite quantity of data streams to discover threats, malfunctions, IT operations failures, and other predictive analytics use cases.

 Behavioral Patterns

Adlumin deploys autonomous parsing of streaming data using advanced algorithms that can detect anomalous events by efficiently learning baseline maps of normal messages, utilizing techniques that are especially scalable for cloud computing environments.

 Full Network Visibility

Adlumin delivers end-to-end visibility into incoming data records through the detection process, giving the user control over the detection and surveillance capability via state-of-the-art and intuitive entry points.

 Visualize Total Metrics

Adlumin visualizes usage, web, and project execution metrics from any system, including Jenkins, using isolation forest and other tree-based techniques within the umbrella of unsupervised machine learning, allowing AF users to direct investigative or analyze resources effectively.

 Graph Theory

Adlumin analyzes user and entity resources using graph theory, giving AF security monitors powerful visuals of how different network elements are connected and aiding further investigation.

 Cluster Analysis

Adlumin automates cluster analysis for use cases that call for quick discovery of outlier instances or for finding coherent groups of observations within a large mass of data, using techniques including K-Nearest-Neighbors (KNN) and Cluster-Based Local Outlier Factor (CBLOF).

 Machine Learning as a Service (MLaaS)

Adlumin can build Machine Learning as a Service (MLaaS) applications covering a broad array of use cases, complementing the cloud computing resources already available within various F35 development environments by delivering turnkey solutions to numerous forecasting and estimation challenges.

 User Command Executions

Adlumin’s model user command executions using PowerShell profiling and codifying the statistical distribution of resulting metrics, leading to detections of abnormal executions without excessive false positives and without arbitrarily discarding analysis-worthy data.

 Easy-to-Understand, Powerful Insights

Adlumin deploys machine learning techniques based on graph-theoretic metrics, such as Principal Components Analysis, to systematically reduce the complexity of the feature space and reveal sharp insights in the data to detect anomalies malfunctions.

Adlumin’s UEBA Features

No Data Limits

Adlumin delivers hyper-scalable patented architecture for ingesting streaming data and continuously training. The platform updates machine learning algorithms for anomaly detection across an infinite quantity of data streams to discover threats, malfunctions, IT operations failures, and other predictive analytics use cases.

Behavioral Patterns

Adlumin deploys autonomous parsing of streaming data using advanced algorithms that can detect anomalous events by efficiently learning baseline maps of normal messages, utilizing techniques that are especially scalable for cloud computing environments.

Full Network Visibility

Adlumin delivers end-to-end visibility into incoming data records through the detection process, giving the user control over the detection and surveillance capability via state-of-the-art and intuitive entry points.

Visualize Total Metrics

Adlumin visualizes usage, web, and project execution metrics from any system, including Jenkins, using isolation forest and other tree-based techniques within the umbrella of unsupervised machine learning, allowing AF users to direct investigative or analyze resources effectively.

Machine Learning as a Service (MLaaS)

Adlumin can build Machine Learning as a Service (MLaaS) applications covering a broad array of use cases, complementing the cloud computing resources already available within various F35 development environments by delivering turnkey solutions to numerous forecasting and estimation challenges.

Cluster Analysis

Adlumin automates cluster analysis for use cases that call for quick discovery of outlier instances or for finding coherent groups of observations within a large mass of data, using techniques including K-Nearest-Neighbors (KNN) and Cluster-Based Local Outlier Factor (CBLOF).

User Command Executions

Adlumin’s model user command executions using PowerShell profiling and codifying the statistical distribution of resulting metrics, leading to detections of abnormal executions without excessive false positives and without arbitrarily discarding analysis-worthy data.

Easy-to-Understand, Powerful Insights

Adlumin deploys machine learning techniques based on graph-theoretic metrics, such as Principal Components Analysis, to systematically reduce the complexity of the feature space and reveal sharp insights in the data to detect anomalies malfunctions.

Graph Theory

Adlumin analyzes user and entity resources using graph theory, giving AF security monitors powerful visuals of how different network elements are connected and aiding further investigation.

Ready to demo?

Schedule a briefing and live demo of Adlumin’s SIEM platform and learn more about key features designed for security and compliance.

Adlumin’s UEBA Features

No Data Limits

Adlumin delivers hyper-scalable patented architecture for ingesting streaming data and continuously training. The platform updates machine learning algorithms for anomaly detection across an infinite quantity of data streams to discover malfunctions, IT operations failures, threats, and  other predictive analytics use cases.

Behavioral Patterns

Adlumin deploys autonomous parsing of streaming data using advanced algorithms that can detect anomalous events by efficiently learning baseline maps of normal messages, utilizing techniques that are especially scalable for cloud computing environments.

Full Network Visibility

Adlumin delivers end-to-end visibility into incoming data records through the detection process, giving the user control over the detection and surveillance capability via state-of-the-art and intuitive entry points.

Visualize Total Metrics

Adlumin visualizes usage, web, and project execution metrics from any system, including Jenkins, using isolation forest and other tree-based techniques within the umbrella of unsupervised machine learning, allowing AF users to direct investigative or analyze resources effectively.

Cluster Analysis

Adlumin automates cluster analysis for use cases that call for quick discovery of outlier instances or for finding coherent groups of observations within a large mass of data, using techniques including K-Nearest-Neighbors (KNN) and Cluster-Based Local Outlier Factor (CBLOF).

Graph Theory

Adlumin analyzes user and entity resources using graph theory, giving AF security monitors powerful visuals of how different network elements are connected and aiding further investigation.

User Command Executions

Adlumin’s model user command executions using PowerShell profiling and codifying the statistical distribution of resulting metrics, leading to detections of abnormal executions without excessive false positives and without arbitrarily discarding analysis-worthy data.

Easy-to-Understand, Powerful Insights

Adlumin deploys machine learning techniques based on graph-theoretic metrics, such as Principal Components Analysis, to systematically reduce the complexity of the feature space and reveal sharp insights in the data to detect anomalies malfunctions.

Machine Learning as a Service (MLaaS)

Adlumin can build Machine Learning as a Service (MLaaS) applications covering a broad array of use cases, complementing the cloud computing resources already available within various F35 development environments by delivering turnkey solutions to numerous forecasting and estimation challenges.