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detecting-anomalous-authentication-patterns

Detect anomalous authentication patterns in identity and access management logs.

2|Updated Apr 14, 2026

Search Tags:#authentication #anomaly-detection #log-analysis #machine-learning #ueba #behavior-analytics #identity-access-management

Ready to run

npx skills add https://github.com/Acczdy/MoZiSec --skill detecting-anomalous-authentication-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: detecting-anomalous-authentication-patterns
Source: https://github.com/Acczdy/MoZiSec/tree/main/iam/.claude/skills/detecting-anomalous-authentication-patterns
Command: npx skills add https://github.com/Acczdy/MoZiSec --skill detecting-anomalous-authentication-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Authentication monitoring often suffers from noisy signals and slow detection of subtle compromises. This Skill leverages UEBA baselines and ML-based anomaly scoring to identify anomalous authentication patterns across identity platforms.

Core Features & Use Cases

UEBA-based anomaly detection across Azure AD, Okta, and Windows AD logs.
Behavioral baselines with per-user risk scoring and context-rich alerts.
Use cases include compromised accounts, impossible travel, brute force, password spraying, and credential stuffing investigations.

Quick Start

Provide authentication logs to the Skill and it will detect anomalous login patterns using UEBA baselines and ML-based scoring.

Frequently Asked Questions about detecting-anomalous-authentication-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect anomalous authentication patterns in identity logs?▼

Detect anomalous authentication patterns by applying UEBA-based security analysis and ML-based scoring to timestamped identity logs, identifying suspicious sign-ins and account compromise across identity providers.

What log formats are required for UEBA behavior analytics?▼

UEBA behavior analytics requires timestamped authentication events containing user, source_ip, location, app, device, and outcome fields to establish behavioral baselines and generate accurate risk scores.

Can I analyze Azure AD and Okta logs for compromised accounts?▼

Analyze Azure AD and Okta logs to detect compromised accounts, impossible travel, brute force, password spraying, and credential stuffing using behavioral baselines and per-user risk scoring.

How does machine learning score anomalous logins?▼

Machine learning scores anomalous logins by comparing timestamped events against established behavioral baselines, generating context-rich alerts for suspicious sign-ins and lateral movement across IdPs.

What is the best way to monitor suspicious sign-ins across IdPs?▼

Monitor suspicious sign-ins across IdPs by leveraging UEBA baselines and ML-based anomaly scoring to reduce noisy signals and detect subtle compromises with context-rich alerts.