Performing User Behavior Analytics

# Performing User Behavior Analytics

## When to Use

Use this skill when:
- SOC teams need to detect compromised accounts through abnormal authentication patterns
- Insider threat programs require behavioral monitoring beyond rule-based detection
- Impossible travel or geographic anomalies indicate credential compromise
- Privileged account monitoring requires baseline deviation detection

**Do not use** as the sole basis for disciplinary action — UEBA findings are indicators requiring investigation, not proof of malicious intent.

## Prerequisites

- SIEM with 30+ days of authentication and access log history for baseline creation
- VPN, O365, and Active Directory authentication logs normalized to CIM
- GeoIP database (MaxMind GeoLite2) for location-based anomaly detection
- Identity enrichment data (department, role, manager, typical work hours)
- Splunk Enterprise Security with UBA module or equivalent UEBA capability

## Workflow

### Step 1: Build User Authentication Baselines

Create behavioral baselines from historical data:

```spl
index=auth sourcetype IN ("o365:management:activity", "vpn_logs", "WinEventLog:Security")
earliest=-30d latest=-1d
| stats dc(src_ip) AS unique_ips,
        dc(src_country) AS unique_countries,
        dc(app) AS unique_apps,
        count AS total_logins,
        earliest(_time) AS first_login,
        latest(_time) AS last_login,
        values(src_country) AS countries,
        avg(eval(strftime(_time, "%H"))) AS avg_login_hour,
        stdev(eval(strftime(_time, "%H"))) AS stdev_login_hour
  by user
| eval avg_daily_logins = round(total_logins / 30, 1)
| eval login_hour_range = round(avg_login_hour, 0)." +/- ".round(stdev_login_hour, 1)." hrs"
| table user, unique_ips, unique_countries, unique_apps, avg_daily_logins,
        login_hour_range, countries
```

### Step 2: Detect Impossible Travel

Identify logins from geographically distant locations within impossible timeframes:

```spl