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NDR platforms for real-time network threat detection, investigation, and automated response to network-based attacks.
Browse 120 network detection and response tools
Network flow & SNMP collector with analytics for traffic visibility.
AI-native NDR for cloud, edge, and hybrid network threat detection.
AI-powered DNS detection & response platform integrating DNSEye, DNSDome & Cyber X-Ray.
AI-based DNS security platform blocking tunneling, malware, and zero-days.
Passive network intelligence platform for gov/defense with real-time visibility.
Polish NDR appliance for network threat detection, forensics & GDPR compliance.
Zeek-based network traffic analysis & IDS platform for enterprise deployments.
Packet-based network observability platform for hybrid environments.
Packet broker, capture & observability suite for hybrid network security.
Lossless packet capture & analysis appliance at 10–200 Gbps line rate.
Modular network observability platform for packet brokering, capture & analytics.
AI-based network threat detection using unsupervised machine learning.
CSP-delivered home network security for IoT and connected devices.
Network abuse management platform for ISPs to automate abuse case handling.
AI-driven NDR for identifying and responding to network threats
DNS-layer network visibility and monitoring with query logging and analytics
Network & app performance monitoring platform with end-to-end visibility
Network visibility and security insights platform for IT environments
Flow-based network monitoring platform for performance and security visibility
NDR platform with DPI for network visibility, threat detection, and investigation
TLS/SSL decryption for network traffic visibility and security analysis
Agentless network visibility platform for security posture management
Continuous full packet capture and forensics for network investigations
Bot detection service that verifies human users through challenges
Common questions about Network Detection and Response tools, selection guides, pricing, and comparisons.
NDR analyzes encrypted traffic metadata without decryption: packet sizes, timing patterns, TLS certificate information, connection frequencies, data transfer volumes, and JA3/JA3S fingerprints. Machine learning models trained on these metadata patterns can detect command-and-control communications, data exfiltration, and lateral movement even in fully encrypted traffic.