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