ReddRadar
Agent skill
performing-user-behavior-analytics
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.
Install this agent skill to your Project
npx add-skill https://github.com/autohandai/community-skills/tree/main/performing-user-behavior-analytics
SKILL.md
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
Step 3: Detect Anomalous Login Timing
Identify logins outside a user's normal working hours:
index=auth action=success earliest=-7d
| eval hour = strftime(_time, "%H")
| eval day_of_week = strftime(_time, "%A")
| eval is_weekend = if(day_of_week IN ("Saturday", "Sunday"), 1, 0)
| eval is_off_hours = if(hour < 6 OR hour > 22, 1, 0)
| join user type=left [
search index=auth action=success earliest=-60d latest=-7d
| eval hour = strftime(_time, "%H")
| stats avg(hour) AS baseline_avg_hour, stdev(hour) AS baseline_stdev_hour,
perc95(hour) AS baseline_latest_hour by user
]
| where (is_off_hours=1 OR is_weekend=1) AND
(hour > baseline_latest_hour + 2 OR hour < baseline_avg_hour - baseline_stdev_hour * 2)
| stats count, values(hour) AS login_hours, values(day_of_week) AS login_days,
values(src_ip) AS source_ips
by user, baseline_avg_hour, baseline_latest_hour
| where count > 0
| sort - count
Step 4: Detect Unusual Data Access Patterns
Monitor for abnormal file or database access volumes:
index=file_access OR index=sharepoint earliest=-24h
| stats sum(bytes) AS total_bytes, dc(file_path) AS unique_files,
count AS access_count by user
| join user type=left [
search index=file_access OR index=sharepoint earliest=-30d latest=-1d
| stats avg(eval(count)) AS baseline_avg_files,
stdev(eval(count)) AS baseline_stdev_files,
avg(eval(sum(bytes))) AS baseline_avg_bytes
by user
]
| eval bytes_gb = round(total_bytes / 1073741824, 2)
| eval z_score_files = round((unique_files - baseline_avg_files) / baseline_stdev_files, 2)
| where z_score_files > 3 OR bytes_gb > 5
| eval anomaly_level = case(
z_score_files > 5, "CRITICAL",
z_score_files > 3, "HIGH",
bytes_gb > 10, "CRITICAL",
bytes_gb > 5, "HIGH",
1=1, "MEDIUM"
)
| sort - z_score_files
| table user, unique_files, bytes_gb, baseline_avg_files, z_score_files, anomaly_level
Step 5: Detect Privilege Abuse Patterns
Monitor privileged account usage anomalies:
index=wineventlog sourcetype="WinEventLog:Security"
(EventCode=4672 OR EventCode=4624 OR EventCode=4648) earliest=-24h
| eval is_privileged = if(EventCode=4672, 1, 0)
| eval is_explicit_cred = if(EventCode=4648, 1, 0)
| stats sum(is_privileged) AS priv_events,
sum(is_explicit_cred) AS explicit_cred_events,
dc(ComputerName) AS unique_hosts,
values(ComputerName) AS hosts_accessed
by TargetUserName, src_ip
| join TargetUserName type=left [
search index=wineventlog EventCode IN (4672, 4624, 4648) earliest=-30d latest=-1d
| stats dc(ComputerName) AS baseline_hosts,
avg(eval(count)) AS baseline_daily_events by TargetUserName
]
| where unique_hosts > baseline_hosts * 2 OR priv_events > baseline_daily_events * 3
| eval risk_score = (unique_hosts / baseline_hosts * 30) + (priv_events / baseline_daily_events * 20)
| sort - risk_score
| table TargetUserName, src_ip, unique_hosts, baseline_hosts, priv_events,
baseline_daily_events, risk_score, hosts_accessed
Step 6: Generate Risk Score and Prioritize Investigation
Aggregate all UEBA signals into a composite risk score:
| inputlookup ueba_impossible_travel.csv
| append [| inputlookup ueba_off_hours_access.csv]
| append [| inputlookup ueba_data_access_anomaly.csv]
| append [| inputlookup ueba_privilege_abuse.csv]
| stats sum(risk_points) AS total_risk,
values(anomaly_type) AS anomaly_types,
dc(anomaly_type) AS anomaly_count
by user
| lookup identity_lookup_expanded identity AS user
OUTPUT department, managedBy, priority AS user_priority
| eval final_risk = total_risk * case(
user_priority="critical", 2.0,
user_priority="high", 1.5,
user_priority="medium", 1.0,
1=1, 0.8
)
| sort - final_risk
| head 20
| table user, department, managedBy, anomaly_types, anomaly_count, total_risk, final_risk
Key Concepts
| Term | Definition |
|---|---|
| UEBA | User and Entity Behavior Analytics — behavioral analysis detecting anomalies against established baselines |
| Impossible Travel | Login events from geographically distant locations within timeframes making physical travel impossible |
| Behavioral Baseline | Statistical profile of normal user activity patterns built from 30-90 days of historical data |
| Z-Score | Statistical measure of how many standard deviations an observation is from the mean — values > 3 indicate anomalies |
| Risk Score | Composite numerical score aggregating multiple behavioral anomalies weighted by asset criticality |
| Peer Group Analysis | Comparing a user's behavior to others in the same department/role to identify outliers |
Tools & Systems
- Splunk UBA: Dedicated User Behavior Analytics module integrating with Splunk ES for ML-driven anomaly detection
- Microsoft Sentinel UEBA: Built-in UEBA capability in Azure Sentinel with entity pages and investigation graphs
- Exabeam Advanced Analytics: Standalone UEBA platform with session stitching and automatic timeline creation
- Securonix: Cloud-native SIEM/UEBA with pre-built behavioral models for insider threat detection
Common Scenarios
- Compromised Account: Impossible travel + off-hours login + unusual app access = likely credential compromise
- Insider Data Theft: Employee accessing 10x normal file volume in notice period before departure
- Privilege Escalation Abuse: Admin account used from unusual location accessing systems outside normal scope
- Shared Account Detection: Service account logging in from multiple geographies simultaneously
- Dormant Account Reactivation: Account with no activity for 90+ days suddenly performing privileged operations
Output Format
UEBA ANOMALY REPORT — Weekly Summary
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Period: 2024-03-11 to 2024-03-17
Users Baselined: 2,847
Anomalies Detected: 23
TOP RISK USERS:
# User Dept Risk Anomalies
1. jsmith Finance 94.5 Impossible travel (NYC->Moscow, 2h), off-hours access, 15GB download
2. admin_svc01 IT Ops 82.0 Login from 12 new IPs, 47 hosts accessed (baseline: 8)
3. mwilson HR 67.3 Off-hours file access (2AM), 3x normal download volume
INVESTIGATION STATUS:
jsmith: Escalated to Tier 2 — possible account compromise (IR-2024-0445)
admin_svc01: Under review — may be new automation deployment (checking with IT Ops)
mwilson: Pending HR context — employee on notice period, monitoring increased
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