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
Authentication log sources (Azure AD/Entra ID sign-in logs, Okta system logs, Active Directory event logs 4624/4625/4648/4768/4771)
SIEM platform (Splunk, Microsoft Sentinel, Elastic SIEM) with at least 90 days of baseline data
GeoIP database for location-based anomaly detection (MaxMind GeoLite2 or IP2Location)
Python 3.9+ with pandas, scikit-learn, and scipy for custom analytics
User identi…
以下为摘要预览，完整内容请查看 GitHub 原文。
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
Authentication log sources (Azure AD/Entra ID sign-in logs, Okta system logs, Active Directory event logs 4624/4625/4648/4768/4771)
SIEM platform (Splunk, Microsoft Sentinel, Elastic SIEM) with at least 90 days of baseline data
GeoIP database for location-based anomaly detection (MaxMind GeoLite2 or IP2Location)
Python 3.9+ with pandas, scikit-learn, and scipy for custom analytics
