Individual skill
detecting-anomalous-authentication-patterns
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
skills/detecting-anomalous-authentication-patterns/SKILL.md
Source commit 54a798831d2266a3ca61ce68a7acb80b81160d57
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- Matches the observed repository HEAD
- Yes
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- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 35,887
- Repository last updated
- Aug 31, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- claude-code Repository topic
- cybersecurity Repository topic
- incident-response Repository topic
- mitre-attack Repository topic
- penetration-testing Repository topic
- red-team Repository topic
- security Repository topic
- cloud-security Repository topic
- malware-analysis Repository topic
- ueba Declared skill tag
- authentication-anomaly Declared skill tag
- impossible-travel Declared skill tag
- brute-force Declared skill tag
- credential-stuffing Declared skill tag
- behavioral-analytics Declared skill tag
- data Derived category
- security Derived category
- education Derived category
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# Detecting Anomalous Authentication Patterns
## When to Use
- Security operations needs to identify compromised accounts from authentication log analysis
- Implementing impossible travel detection to flag geographically inconsistent logins
- Detecting brute force, password spraying, and credential stuffing attacks in real time
- Building behavioral baselines for users to identify deviations indicating account compromise
- Correlating authentication anomalies with threat intelligence for lateral movement detection
- Investigating alerts from SIEM or IdP for suspicious sign-in activity
**Do not use** for static rule-based alerting on single failed logins; anomaly detection requires statistical baselines across time and entity dimensions to reduce false positives.
## Prerequisites