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PublishedAugust 9, 2026 at 11:46 PM

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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

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

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:

spl

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:

spl

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:

spl

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:

spl

| 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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