Anomaly Detection
Browse 695 anomaly detection tools
FEATURED
AI-powered observability platform for IT infrastructure monitoring
Zero Trust Network Access platform for cloud, on-premises, and hybrid apps
Detects and prevents insider threats with visibility into risky user behavior
Platform for monitoring, governing, and remediating AI agent actions
WAF protecting web apps and APIs from OWASP Top 10, bots, and DDoS attacks
AI platform for autonomous operations mgmt in industrial & supply chain envs
Protocol-aware reverse proxy for datastores & APIs enforcing access policies
Cloud identity security platform for human, machine, and AI identities
AI platform for mobility cybersecurity and vehicle quality monitoring
CI/CD security platform for GitHub Actions with runtime threat detection
Protects against account abuse across lifecycle using ML and risk indicators
API security platform for discovery, testing, and protection of APIs
Bot detection & mitigation platform protecting against abuse & scraping
AI-powered security platform for threat detection, automation, and AI protection
A Zeek-based protocol analyzer that parses GQUIC traffic to extract connection metadata and create fingerprints for detecting anomalous network behavior.
A defense-in-depth security automation framework for AWS that combines threat intelligence, machine learning, and serverless technologies to prevent, detect, and respond to threats through automated security telemetry collection and analysis.
A repository of officially managed detection rules for the Falco runtime security monitoring system that identifies threats, abnormal behaviors, and compliance violations through syscall and container event analysis.
A low interaction client honeypot that detects malicious websites using signature, anomaly and pattern matching techniques with automated URL collection and JavaScript analysis capabilities.
ElastAlert is a framework for alerting on anomalies in Elasticsearch data.
Netcap efficiently converts network packets into structured audit records for machine learning algorithms, using Protocol Buffers for encoding.
A machine learning-based approach to detect and prevent data breaches using natural language processing and machine learning algorithms.
