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.
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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.
Do not use as the sole basis for disciplinary action — UEBA findings are indicators requiring investigation, not proof of malicious intent.
Detection Gaps & Validation
Baseline poisoning: if the 30–90 day window used to build avg/stdev already contains the attacker's activity (a slow-burn insider, a long-dwell compromised account), their behavior becomes "normal" and the z-score never trips. Validate the baseline window is clean, and rebuild baselines from a known-good period when investigating a confirmed incident.
Wrong / too-broad peer group: comparing a user only against their own history misses someone who was abnormal from day one — peer-group analysis (same department/role/manager) catches the outlier the self-baseline hides. Conversely, a peer group that lumps admins with standard users flags every admin as anomalous. Confirm identity_lookup_expanded department/role is accurate before trusting peer comparisons.
Zero or tiny stdev: users with very regular patterns get stdev≈0, so avg + 3*stdev fires on trivial deviation (FP storm); new/low-activity accounts have too few samples for a stable baseline. Require a minimum event count and floor the stdev.
Impossible-travel artifacts: corporate VPN egress, cloud/CDN IPs, and mobile carrier CGNAT make GeoIP jump continents legitimately; a single mis-geolocated src_ip fabricates a 900 km/h "trip." Exclude known VPN/proxy ranges and verify against MaxMind accuracy before escalating.
Validate the verdict: UEBA produces leads, not conclusions — correlate the anomaly with corroborating signals (auth success after the travel, actual data movement, HR/notice-period context) and rule out service accounts, automation, and shared accounts before raising the risk score. Stitch identifiers (UPN vs SamAccountName vs email) so one human isn't scored as several low-signal entities.
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
