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.
internal/tools/skills/data/identity-access-management/detecting-anomalous-authentication-patterns/SKILL.md
Source commit d42f381ad34843e71a42658e5c57290c713e85af
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 1,322
- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing 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.
## Detection Gaps & Validation