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detecting-anomalous-authentication-patterns Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
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name detecting-anomalous-authentication-patterns description Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
domain cybersecurity subdomain identity-access-management tags ["UEBA","authentication-anomaly","impossible-travel","brute-force","credential-stuffing","behavioral-analytics"] version 1.0 author mahipal license Apache-2.0 atlas_techniques ["AML.T0043","AML.T0018"] nist_ai_rmf ["MEASURE-2.7","MEASURE-2.5","MAP-5.1"] nist_csf ["PR.AA-01","PR.AA-02","PR.AA-05","PR.AA-06"] mitre_attack ["T1110","T1110.003","T1110.004","T1078","T1021"] source https://github.com/mukul975/Anthropic-Cybersecurity-Skills source_commit 04450304b12645cb2b974ab96d28c0664758a88d note Vendored verbatim from an external Apache-2.0 security-skill library, pinned by commit. Exceeds the internal 300-line skill guideline (agent-code-constraints.md) -- kept as-is because this is vendored reference material (forensics/threat-intel procedure), not Yana AI-authored content, and trimming would damage technical accuracy.
Detecting Anomalous Authentication Patterns
When to Use
Security operations needs to identify compromised accounts from authentication log analysis
Implementing impossible travel detection to flag geographically inconsistent logins
Detecting brute force, password spraying, and credential stuffing attacks in real time
Building behavioral baselines for users to identify deviations indicating account compromise
Correlating authentication anomalies with threat intelligence for lateral movement detection
Investigating alerts from SIEM or IdP for suspicious sign-in activity
Do not use for static rule-based alerting on single failed logins; anomaly detection requires statistical baselines across time and entity dimensions to reduce false positives.
Prerequisites
Authentication log sources (Azure AD/Entra ID sign-in logs, Okta system logs, Active Directory event logs 4624/4625/4648/4768/4771)
SIEM platform (Splunk, Microsoft Sentinel, Elastic SIEM) with at least 90 days of baseline data
GeoIP database for location-based anomaly detection (MaxMind GeoLite2 or IP2Location)
Python 3.9+ with pandas, scikit-learn, and scipy for custom analytics
User identity context (department, role, typical work hours, location)
Workflow
Step 1: Collect and Normalize Authentication Logs
Aggregate authentication events from all identity sources:
import pandas as pd
import json
from datetime import datetime, timedelta
from collections import defaultdict
def normalize_auth_logs (log_source, raw_logs ):
"""Normalize authentication events to a common schema."""
normalized = []
for event in raw_logs:
if log_source == "azure_ad" :
normalized.append({
"timestamp" : event["createdDateTime" ],
"user" : event["userPrincipalName"
"source_ip"
"ipAddress"
"location"
"city"
"location"
"city"
"state"
"location"
"state"
"country"
"location"
"countryOrRegion"
"lat"
"location"
"geoCoordinates"
"latitude"
"lon"
"location"
"geoCoordinates"
"longitude"
"result"
"success"
if
"status"
"errorCode"
0
else
"failure"
"failure_reason"
"status"
"failureReason"
""
"app"
"appDisplayName"
"Unknown"
"device"
"deviceDetail"
"operatingSystem"
"Unknown"
"browser"
"deviceDetail"
"browser"
"Unknown"
"mfa_result"
"authenticationDetails"
0
"succeeded"
None
"risk_level"
"riskLevelDuringSignIn"
"none"
"client_app"
"clientAppUsed"
"Unknown"
"source"
"azure_ad"
elif
"okta"
"timestamp"
"published"
"user"
"actor"
"alternateId"
"source_ip"
"client"
"ipAddress"
"location"
"city"
"client"
"geographicalContext"
"city"
"state"
"client"
"geographicalContext"
"state"
"country"
"client"
"geographicalContext"
"country"
"lat"
"client"
"geographicalContext"
"geolocation"
"lat"
"lon"
"client"
"geographicalContext"
"geolocation"
"lon"
"result"
"success"
if
"outcome"
"result"
"SUCCESS"
else
"failure"
"failure_reason"
"outcome"
"reason"
""
"app"
"target"
0
"displayName"
"Unknown"
"device"
"client"
"device"
"Unknown"
"browser"
"client"
"userAgent"
"browser"
"Unknown"
"source"
"okta"
elif
"windows_ad"
"timestamp"
"TimeCreated"
"user"
"TargetUserName"
"source_ip"
"IpAddress"
""
"location"
None
"result"
"success"
if
"EventId"
in
4624
4648
else
"failure"
"failure_reason"
"FailureReason"
""
"logon_type"
"LogonType"
""
"source"
"windows_ad"
return
import
def
enrich_geoip
df, geoip_db_path="/opt/geoip/GeoLite2-City.mmdb"
"""Add geolocation data to events missing location information."""
for
in
if
"location"
is
None
and
"source_ip"
try
"source_ip"
"location"
"city"
"country"
"lat"
"lon"
except
pass
return
Step 2: Detect Impossible Travel Anomalies Identify logins from geographically impossible locations:
from math import radians, sin, cos, sqrt, atan2
from datetime import datetime
def haversine_distance (lat1, lon1, lat2, lon2 ):
"""Calculate great-circle distance between two points in km."""
R = 6371
lat1, lon1, lat2, lon2 = map (radians, [lat1, lon1, lat2, lon2])
dlat = lat2 - lat1
dlon = lon2 - lon1
a = sin(dlat/2 )**2 + cos(lat1) * cos(lat2) * sin(dlon/2 )**2
c = 2 * atan2(sqrt(a), sqrt(1 -a))
return R * c
def detect_impossible_travel (df, max_speed_kmh=900 ):
"""
Detect impossible travel events where a user authenticates from
two locations faster than physically possible.
max_speed_kmh: Maximum realistic travel speed (900 km/h ~= commercial flight)
"""
alerts = []
df_sorted = df.sort_values(["user" , "timestamp" ])
for user, user_events in df_sorted.groupby("user" ):
successful_events = user_events[user_events["result" ] == "success" ]
for i in range (1 , len (successful_events)):
prev = successful_events.iloc[i-1 ]
curr = successful_events.iloc[i]
if not prev.get("location" ) or not curr.get("location" ):
continue
if not prev["location" ].get("lat" ) or not curr["location" ].get("lat" ):
continue
distance_km = haversine_distance(
prev["location" ]["lat" ], prev["location" ]["lon" ],
curr["location" ]["lat" ], curr["location" ]["lon" ]
)
time_diff = (pd.Timestamp(curr["timestamp" ]) -
pd.Timestamp(prev["timestamp" ])).total_seconds() / 3600
if time_diff <= 0 :
continue
required_speed = distance_km / time_diff
if required_speed > max_speed_kmh and distance_km > 100 :
alerts.append({
"alert_type" : "IMPOSSIBLE_TRAVEL" ,
"severity" : "HIGH" ,
"user" : user,
"timestamp" : curr["timestamp" ],
"details" : {
"location_1" : f"{prev['location' ]['city' ]} , {prev['location' ]['country' ]} " ,
"location_2" : f"{curr['location' ]['city' ]} , {curr['location' ]['country' ]} " ,
"time_1" : prev["timestamp" ],
"time_2" : curr["timestamp" ],
"distance_km" : round (distance_km, 1 ),
"time_hours" : round (time_diff, 2 ),
"required_speed_kmh" : round (required_speed, 1 ),
"source_ip_1" : prev["source_ip" ],
"source_ip_2" : curr["source_ip" ]
}
})
return alerts
travel_alerts = detect_impossible_travel(auth_df)
print (f"Impossible travel alerts: {len (travel_alerts)} " )
for alert in travel_alerts:
print (f" [{alert['severity' ]} ] {alert['user' ]} : "
f"{alert['details' ]['location_1' ]} -> {alert['details' ]['location_2' ]} "
f"({alert['details' ]['distance_km' ]} km in {alert['details' ]['time_hours' ]} h)" )
Step 3: Detect Brute Force and Password Spraying Identify credential attack patterns across authentication logs:
from collections import Counter
def detect_brute_force (df, threshold_failures=10 , window_minutes=10 ):
"""
Detect brute force attacks: many failed attempts against
a single account in a short time window.
"""
alerts = []
failed = df[df["result" ] == "failure" ].copy()
failed["timestamp" ] = pd.to_datetime(failed["timestamp" ])
for user, user_fails in failed.groupby("user" ):
user_fails_sorted = user_fails.sort_values("timestamp" )
for i, row in user_fails_sorted.iterrows():
window_start = row["timestamp" ]
window_end = window_start + timedelta(minutes=window_minutes)
window_events = user_fails_sorted[
(user_fails_sorted["timestamp" ] >= window_start) &
(user_fails_sorted["timestamp" ] <= window_end)
]
if len (window_events) >= threshold_failures:
source_ips = window_events["source_ip" ].unique()
alerts.append({
"alert_type" : "BRUTE_FORCE" ,
"severity" : "HIGH" ,
"user" : user,
"timestamp" : str (window_start),
"details" : {
"failed_attempts" : len (window_events),
"window_minutes" : window_minutes,
"source_ips" : list (source_ips),
"distributed" : len (source_ips) > 1 ,
"failure_reasons" : dict (Counter(window_events["failure_reason" ]))
}
})
break
return alerts
def detect_password_spray (df, threshold_users=10 , window_minutes=30 ):
"""
Detect password spraying: failed logins against many different
accounts from the same source in a short window (1-2 attempts per user).
"""
alerts = []
failed = df[df["result" ] == "failure" ].copy()
failed["timestamp" ] = pd.to_datetime(failed["timestamp" ])
for source_ip, ip_events in failed.groupby("source_ip" ):
ip_events_sorted = ip_events.sort_values("timestamp" )
for i, row in ip_events_sorted.iterrows():
window_start = row["timestamp" ]
window_end = window_start + timedelta(minutes=window_minutes)
window_events = ip_events_sorted[
(ip_events_sorted["timestamp" ] >= window_start) &
(ip_events_sorted["timestamp" ] <= window_end)
]
unique_users = window_events["user" ].nunique()
attempts_per_user = len (window_events) / unique_users if unique_users > 0 else 0
if unique_users >= threshold_users and attempts_per_user <= 3 :
success_after = df[
(df["source_ip" ] == source_ip) &
(df["result" ] == "success" ) &
(pd.to_datetime(df["timestamp" ]) > window_start) &
(pd.to_datetime(df["timestamp" ]) < window_end + timedelta(hours=1 ))
]
alerts.append({
"alert_type" : "PASSWORD_SPRAY" ,
"severity" : "CRITICAL" if len (success_after) > 0 else "HIGH" ,
"timestamp" : str (window_start),
"details" : {
"source_ip" : source_ip,
"targeted_users" : unique_users,
"total_attempts" : len (window_events),
"avg_attempts_per_user" : round (attempts_per_user, 1 ),
"window_minutes" : window_minutes,
"successful_logins_after" : len (success_after),
"compromised_accounts" : list (success_after["user" ].unique()) if len (success_after) > 0 else []
}
})
break
return alerts
brute_force_alerts = detect_brute_force(auth_df)
spray_alerts = detect_password_spray(auth_df)
print (f"Brute force alerts: {len (brute_force_alerts)} " )
print (f"Password spray alerts: {len (spray_alerts)} " )
Step 4: Build Behavioral Baselines and Detect Deviations Create user behavioral profiles and flag statistical anomalies:
import numpy as np
from scipy import stats
from sklearn.ensemble import IsolationForest
def build_user_baseline (df, user, lookback_days=90 ):
"""Build behavioral baseline for a specific user."""
user_events = df[df["user" ] == user].copy()
user_events["timestamp" ] = pd.to_datetime(user_events["timestamp" ])
user_events["hour" ] = user_events["timestamp" ].dt.hour
user_events["day_of_week" ] = user_events["timestamp" ].dt.dayofweek
baseline = {
"user" : user,
"typical_hours" : {
"start" : int (user_events["hour" ].quantile(0.05 )),
"end" : int (user_events["hour" ].quantile(0.95 )),
"mean" : float (user_events["hour" ].mean()),
"std" : float (user_events["hour" ].std())
},
"typical_days" : list (user_events["day_of_week" ].mode().values),
"typical_ips" : list (user_events["source_ip" ].value_counts().head(10 ).index),
"typical_locations" : list (
user_events["location" ].apply(
lambda x: x.get("country" ) if isinstance (x, dict ) else None
).dropna().value_counts().head(5 ).index
),
"typical_apps" : list (user_events["app" ].value_counts().head(10 ).index),
"typical_devices" : list (user_events["device" ].value_counts().head(5 ).index),
"avg_daily_logins" : float (
user_events.groupby(user_events["timestamp" ].dt.date).size().mean()
),
"std_daily_logins" : float (
user_events.groupby(user_events["timestamp" ].dt.date).size().std()
),
"failure_rate" : float (
(user_events["result" ] == "failure" ).mean()
)
}
return baseline
def detect_behavioral_anomalies (event, baseline ):
"""Compare a new authentication event against user baseline."""
anomalies = []
event_time = pd.Timestamp(event["timestamp" ])
hour = event_time.hour
if baseline["typical_hours" ]["std" ] > 0 :
z_score = abs (hour - baseline["typical_hours" ]["mean" ]) / baseline["typical_hours" ]["std" ]
if z_score > 2.5 :
anomalies.append({
"type" : "OFF_HOURS_LOGIN" ,
"severity" : "MEDIUM" ,
"detail" : f"Login at {hour} :00 (baseline: {baseline['typical_hours' ]['start' ]} :00-{baseline['typical_hours' ]['end' ]} :00)" ,
"z_score" : round (z_score, 2 )
})
if event["source_ip" ] not in baseline["typical_ips" ]:
anomalies.append({
"type" : "NEW_SOURCE_IP" ,
"severity" : "MEDIUM" ,
"detail" : f"Login from unknown IP: {event['source_ip' ]} "
})
if event.get("location" ) and isinstance (event["location" ], dict ):
country = event["location" ].get("country" )
if country and country not in baseline["typical_locations" ]:
anomalies.append({
"type" : "NEW_COUNTRY" ,
"severity" : "HIGH" ,
"detail" : f"Login from new country: {country} "
})
if event.get("app" ) and event["app" ] not in baseline["typical_apps" ]:
anomalies.append({
"type" : "NEW_APPLICATION" ,
"severity" : "LOW" ,
"detail" : f"Access to new application: {event['app' ]} "
})
if event.get("device" ) and event["device" ] not in baseline["typical_devices" ]:
anomalies.append({
"type" : "NEW_DEVICE" ,
"severity" : "MEDIUM" ,
"detail" : f"Login from new device: {event['device' ]} "
})
if event_time.dayofweek >= 5 and 5 not in baseline["typical_days" ] and 6 not in baseline["typical_days" ]:
anomalies.append({
"type" : "WEEKEND_LOGIN" ,
"severity" : "LOW" ,
"detail" : f"Weekend login detected (typical days: {baseline['typical_days' ]} )"
})
return anomalies
def isolation_forest_anomaly_detection (df ):
"""Use Isolation Forest for multivariate anomaly detection."""
features_df = df.copy()
features_df["timestamp" ] = pd.to_datetime(features_df["timestamp" ])
features_df["hour" ] = features_df["timestamp" ].dt.hour
features_df["day_of_week" ] = features_df["timestamp" ].dt.dayofweek
features_df["is_failure" ] = (features_df["result" ] == "failure" ).astype(int )
features_df["ip_frequency" ] = features_df.groupby("source_ip" )["source_ip" ].transform("count" )
features_df["user_frequency" ] = features_df.groupby("user" )["user" ].transform("count" )
feature_columns = ["hour" , "day_of_week" , "is_failure" , "ip_frequency" , "user_frequency" ]
X = features_df[feature_columns].fillna(0 )
model = IsolationForest(
n_estimators=200 ,
contamination=0.01 ,
random_state=42 ,
n_jobs=-1
)
features_df["anomaly_score" ] = model.fit_predict(X)
features_df["anomaly_probability" ] = model.score_samples(X)
anomalies = features_df[features_df["anomaly_score" ] == -1 ]
return anomalies.sort_values("anomaly_probability" )
Step 5: Implement SIEM Detection Rules Deploy detection rules for common authentication attack patterns:
brute_force_spl: |
index=auth sourcetype IN ("azure:aad:signin", "okta:im:log", "WinEventLog:Security")
(result="failure" OR EventCode=4625)
| bin _time span=10m
| stats count as failed_attempts dc(src_ip) as unique_ips
values(src_ip) as source_ips
latest(_time) as last_attempt
by user _time
| where failed_attempts >= 10
| eval alert_type=if(unique_ips > 3, "Distributed Brute Force", "Standard Brute Force")
password_spray_spl: |
index=auth sourcetype IN ("azure:aad:signin", "okta:im:log")
result="failure"
| bin _time span=30m
| stats dc(user) as targeted_users count as total_attempts
values(user) as users_targeted
by src_ip _time
| where targeted_users >= 10
| eval attempts_per_user = round(total_attempts / targeted_users, 1)
| where attempts_per_user <= 3
| eval severity=if(targeted_users > 50, "CRITICAL", "HIGH")
impossible_travel_spl: |
index=auth result="success"
| 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
by user
| where isnotnull(prev_lat) AND isnotnull(lat)
| eval distance_km = 6371 * 2 * asin(sqrt(
pow(sin((lat - prev_lat) * pi() / 360), 2) +
cos(prev_lat * pi() / 180) * cos(lat * pi() / 180) *
pow(sin((lon - prev_lon) * pi() / 360), 2)))
| eval time_hours = (_time - prev_time) / 3600
| eval required_speed = distance_km / time_hours
| where required_speed > 900 AND distance_km > 100
credential_stuffing_spl: |
index=auth
| bin _time span=1h
| stats count(eval(result="failure")) as failures
count(eval(result="success")) as successes
dc(user) as unique_users
dc(src_ip) as unique_ips
by src_ip _time
| where failures > 100 AND successes > 0 AND unique_users > 20
| eval success_rate = round(successes / (failures + successes) * 100, 2)
| where success_rate < 5
Step 6: Correlate and Score Authentication Anomalies Combine multiple detection signals into risk scores:
def calculate_auth_risk_score (user, alerts, baseline ):
"""
Calculate composite risk score for authentication events.
Combines multiple anomaly signals with weighted scoring.
"""
score = 0
risk_factors = []
weights = {
"IMPOSSIBLE_TRAVEL" : 40 ,
"PASSWORD_SPRAY" : 35 ,
"BRUTE_FORCE" : 30 ,
"CREDENTIAL_STUFFING" : 35 ,
"NEW_COUNTRY" : 25 ,
"OFF_HOURS_LOGIN" : 15 ,
"NEW_SOURCE_IP" : 10 ,
"NEW_DEVICE" : 10 ,
"NEW_APPLICATION" : 5 ,
"WEEKEND_LOGIN" : 5 ,
"MFA_BYPASS" : 45 ,
"LEGACY_PROTOCOL" : 20
}
for alert in alerts:
alert_type = alert.get("type" ) or alert.get("alert_type" )
weight = weights.get(alert_type, 10 )
severity_multiplier = {
"CRITICAL" : 2.0 ,
"HIGH" : 1.5 ,
"MEDIUM" : 1.0 ,
"LOW" : 0.5
}
severity = alert.get("severity" , "MEDIUM" )
adjusted_weight = weight * severity_multiplier.get(severity, 1.0 )
score += adjusted_weight
risk_factors.append({
"factor" : alert_type,
"weight" : adjusted_weight,
"detail" : alert.get("detail" , alert.get("details" , "" ))
})
normalized_score = min (100 , score)
if normalized_score >= 80 :
risk_level = "CRITICAL"
recommended_action = "Immediate account suspension and investigation"
elif normalized_score >= 60 :
risk_level = "HIGH"
recommended_action = "Force MFA re-enrollment and notify SOC"
elif normalized_score >= 40 :
risk_level = "MEDIUM"
recommended_action = "Require step-up authentication"
elif normalized_score >= 20 :
risk_level = "LOW"
recommended_action = "Monitor and log for trend analysis"
else :
risk_level = "INFORMATIONAL"
recommended_action = "No action required"
return {
"user" : user,
"risk_score" : normalized_score,
"risk_level" : risk_level,
"recommended_action" : recommended_action,
"risk_factors" : sorted (risk_factors, key=lambda x: x["weight" ], reverse=True ),
"timestamp" : datetime.utcnow().isoformat()
}
Key Concepts Term Definition Impossible Travel Authentication anomaly where a user logs in from two geographically distant locations within a timeframe that makes physical travel impossible Password Spraying Credential attack that tries a small number of commonly used passwords against many accounts to avoid lockout thresholds Credential Stuffing Automated attack using stolen username/password pairs from data breaches to gain unauthorized access to accounts UEBA User and Entity Behavior Analytics technology that builds behavioral baselines and detects deviations using machine learning and statistical analysis Behavioral Baseline Statistical profile of a user's normal authentication patterns including typical hours, locations, devices, and applications Isolation Forest Unsupervised machine learning algorithm that detects anomalies by isolating observations that differ from the majority of data points Risk Score Composite numerical value aggregating multiple anomaly signals with weighted scoring to prioritize authentication threats
Tools & Systems
Microsoft Sentinel UEBA : Cloud-native SIEM with built-in entity behavior analytics for Azure AD and multi-cloud authentication anomaly detectionExabeam Advanced Analytics : UEBA platform using machine learning for user session analysis and automated threat timeline constructionSplunk UBA : Behavioral analytics add-on for Splunk providing pre-built authentication anomaly models and risk scoringElastic SIEM ML Jobs : Machine learning anomaly detection jobs for authentication log analysis in the Elastic Stack
Common Scenarios
Scenario: Detecting Compromised Executive Account After Password Spray Context : SOC observes a spike in failed authentication attempts from a cloud VPS IP address targeting 200+ accounts. Two hours later, an executive account shows successful authentication from the same IP range followed by mailbox rule creation and data exfiltration.
Run password spray detection across the timeframe to identify all targeted accounts
Cross-reference targeted accounts with subsequent successful logins from related IP ranges
Build behavioral baseline for the executive account and flag all deviations
Check for impossible travel between the executive's last legitimate login and the attacker's session
Identify post-compromise activity: mailbox rules, file downloads, delegated access changes
Calculate composite risk score combining password spray, new IP, off-hours login, and new device signals
Trigger automated response: force session termination, disable account, notify manager
Relying on single-signal detection (failed logins only) misses successful spray results
Not correlating across identity providers when users have accounts in multiple IdPs
Static thresholds that do not account for legitimate VPN IP changes or travel
Ignoring successful authentications after the spray window closes (attackers may wait before using credentials)
Output Format AUTHENTICATION ANOMALY DETECTION REPORT
=========================================
Analysis Period: 2026-02-01 to 2026-02-24
Total Auth Events: 2,847,392
Users Monitored: 3,847
Alert Sources: Azure AD, Okta, Windows AD
THREAT DETECTION SUMMARY
Password Spray Attacks: 3
Brute Force Attacks: 12
Impossible Travel: 8
Credential Stuffing: 1
Behavioral Anomalies: 47
HIGH-RISK ACCOUNTS
[CRITICAL] j.smith@corp.com Score: 92
- Impossible travel: Chicago -> Moscow (7,876 km in 0.5h)
- Password spray target followed by successful login
- New device and browser fingerprint
- Off-hours access to SharePoint and email
Action: Account suspended, SOC investigation initiated
[HIGH] m.johnson@corp.com Score: 67
- Login from new country (Brazil)
- New source IP not matching VPN ranges
- Access to HR application outside normal pattern
Action: MFA re-enrollment required, manager notified
[MEDIUM] a.williams@corp.com Score: 38
- Weekend login at 03:00 UTC
- New device (Linux, typically Windows user)
Action: Step-up authentication applied
ATTACK CAMPAIGN DETAILS
Password Spray Campaign #1:
Source: 185.220.101.x/24 (Tor exit node)
Targeted Users: 247
Success Rate: 0.8% (2 accounts compromised)
Compromised: j.smith@corp.com, r.davis@corp.com
Duration: 45 minutes
Pattern: 2 attempts per user, 3-second interval
