ExtraHop RevealX vs OpenUBA

ExtraHop RevealX combines network detection and response, network performance management, intrusion detection, and packet forensics for enterprise security and IT teams. It analyzes packets and applications across network conversations using decryption, packet-level investigation, and cloud-scale machine learning. ExtraHop says out-of-band decryption handles up to 100 Gbps across more than 90 network and application protocols. RevealX detects encrypted threats and lateral movement; cloud-updated intrusion detection signatures cover known attacks. Packet Forensics supports capture across hybrid environments, global search, and packet viewing and analysis in the same platform, with packets retained alongside context. Deployment can be cloud-delivered or self-hosted with physical or virtual sensors; named options are SaaS RevealX 360 and on-premises RevealX Enterprise. Listed integrations include AWS, Jira, CrowdStrike, Cisco, Google Cloud, and Datadog. Subscriptions include Learning Hub access and self-directed learning and certification. Standard technical support is included, with 24/5 global coverage and weekend support for critical issues. Pricing is available on request, and a free trial is offered without a stated duration.

OpenUBA is a free, open-source User and Entity Behavior Analytics framework for security analytics. It analyzes behavior with machine-learning models to identify anomalies, assign risk scores, and produce classifications. The dashboard covers entity monitoring, risk trends, job orchestration, case management, and real-time behavioral baselines for tracking 10,000+ users. Data ingestion supports Spark, Elasticsearch, and CSV, and the project describes the platform as SIEM agnostic. Rule Studio provides a visual flow-graph builder for detection logic, while matching rules can trigger real-time alerts. Cases can be triaged, escalated, and resolved in built-in workflows. Models use a model.yaml configuration and MODEL.py interface with train() and infer() methods; examples use scikit-learn, TensorFlow, PyTorch, Keras, and NetworkX. A Kubernetes-native operator runs jobs in ephemeral containers, with GraphQL logs and monitoring. Deployment is self-hosted on Kind or production Kubernetes clusters.