| name | performing-user-behavior-analytics |
| description | Performs User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.
|
| domain | cybersecurity |
| subdomain | soc-operations |
| tags | ["soc","ueba","user-behavior","insider-threat","anomaly-detection","splunk","baseline"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | ["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"] |
| mitre_attack | ["T1078","T1685.002","T1685.005","T1566","T0816"] |
| source | https://github.com/mukul975/Anthropic-Cybersecurity-Skills |
| source_commit | 04450304b12645cb2b974ab96d28c0664758a88d |
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:
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:
index=auth sourcetype IN ("o365:management:activity", "vpn_logs")
action=success earliest=-24h
| iplocation src_ip
| sort user, _time
| streamstats current=f last(lat) AS prev_lat, last(lon) AS prev_lon,
last(_time) AS prev_time, last(City) AS prev_city,
last(Country) AS prev_country, last(src_ip) AS prev_ip
by user
| where isnotnull(prev_lat)
| eval distance_km = round(
6371 * acos(
cos(pi()/180 * lat) * cos(pi()/180 * prev_lat) *
cos(pi()/180 * (lon - prev_lon)) +
sin(pi()/180 * lat) * sin(pi()/180 * prev_lat)
), 0)
| eval time_diff_hours = round((_time - prev_time) / 3600, 2)
| eval speed_kmh = if(time_diff_hours > 0, round(distance_km / time_diff_hours, 0), 0)
| where speed_kmh > 900 AND distance_km > 500
| eval alert = "IMPOSSIBLE TRAVEL: ".prev_city.", ".prev_country." -> ".City.", ".Country
| table _time, user, prev_city, prev_country, City, Country, distance_km,
time_diff_hours, speed_kmh, alert
| sort - speed_kmh
