Splunk User Behavior Analytics vs Trellix Intrusion Prevention System comparison

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.

Block More Intrusions
Stop new and unknown attacks with signature-based and signature-less intrusion prevention systems. Signature-less intrusion detection finds malicious network traffic and stops attacks where no signatures exist.

Unify Virtual and Physical Security
Support network virtualization across private and public cloud platforms to scale security and evolve with changing IT dynamics.

Maximize Security and Performance
Scale hardware performance to speeds up to 100 Gbps and leverage data from multiple products.