LogRhythm NetworkXDR [EOL] vs Splunk User Behavior Analytics comparison

LogRhythm NDR is a network security solution for detecting, qualifying, investigating, and responding to advanced network-borne threats. It provides the speed and full network visibility needed to combat attacks across your on-premise, remote, and cloud environments.

The solution surfaces these threats through centralized, machine-based analysis of network traffic, including TTP scenario-based modeling, IOC signature-based inspection, and behavioral analysis.

LogRhythm NDR leverages the power and capabilities of the LogRhythm NextGen SIEM platform, including patented and award-winning security analytics and embedded SOAR functionality.

LogRhythm NDR has enabled customers to successfully catch, investigate, and respond to an array of threats such as low and slow data leaks, compromised servers used in lateral attacks, privileged user reconnaissance within internal networks, and SCADA attacks.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.