detecting-anomalous-authentication-patterns

What this skill does

# 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:

```python
import pandas as pd
import json
from datetime import datetime, timedelta
from collections import defaultdict

# Parse authentication logs from multiple sources
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": event["ipAddress"],
                "location": {
                    "city": event.get("location", {}).get("city"),
                    "state": event.get("location", {}).get("state"),
                    "country": event.get("location", {}).get("countryOrRegion"),
                    "lat": event.get("location", {}).get("geoCoordinates", {}).get("latitude"),
                    "lon": event.get("location", {}).get("geoCoordinates", {}).get("longitude")
                },
                "result": "success" if event["status"]["errorCode"] == 0 else "failure",
                "failure_reason": event["status"].get("failureReason", ""),
                "app": event.get("appDisplayName", "Unknown"),
                "device": event.get("deviceDetail", {}).get("operatingSystem", "Unknown"),
                "browser": event.get("deviceDetail", {}).get("browser", "Unknown"),
                "mfa_result": event.get("authenticationDetails", [{}])[0].get("succeeded", None),
                "risk_level": event.get("riskLevelDuringSignIn", "none"),
                "client_app": event.get("clientAppUsed", "Unknown"),
                "source": "azure_ad"
            })
        elif log_source == "okta":
            normalized.append({
                "timestamp": event["published"],
                "user": event["actor"]["alternateId"],
                "source_ip": event["client"]["ipAddress"],
                "location": {
                    "city": event["client"].get("geographicalContext", {}).get("city"),
                    "state": event["client"].get("geographicalContext", {}).get("state"),
                    "country": event["client"].get("geographicalContext", {}).get("country"),
                    "lat": event["client"].get("geographicalContext", {}).get("geolocation", {}).get("lat"),
                    "lon": event["client"].get("geographicalContext", {}).get("geolocation", {}).get("lon")
                },
                "result": "success" if event["outcome"]["result"] == "SUCCESS" else "failure",
                "failure_reason": event["outcome"].get("reason", ""),
                "app": event.get("target", [{}])[0].get("displayName", "Unknown"),
                "device": event["client"].get("device", "Unknown"),
                "browser": event["client"].get("userAgent", {}).get("browser", "Unknown"),
                "source": "okta"
            })
        elif log_source == "windows_ad":
            normalized.append({
                "timestamp": event["TimeCreated"],
                "user": event["TargetUserName"],
                "source_ip": event.get("IpAddress", ""),
                "location": None,  # Requires GeoIP enrichment
                "result": "success" if event["EventId"] in [4624, 4648] else "failure",
                "failure_reason": event.get("FailureReason", ""),
                "logon_type": event.get("LogonType", ""),
                "source": "windows_ad"
            })

    return pd.DataFrame(normalized)

# Enrich with GeoIP data for Windows AD logs missing location
import geoip2.database

def enrich_geoip(df, geoip_db_path="/opt/geoip/GeoLite2-City.mmdb"):
    """Add geolocation data to events missing location information."""
    reader = geoip2.database.Reader(geoip_db_path)

    for idx, row in df.iterrows():
        if row["location"] is None and row["source_ip"]:
            try:
                response = reader.city(row["source_ip"])
                df.at[idx, "location"] = {
                    "city": response.city.name,
                    "country": response.country.iso_code,
                    "lat": response.location.latitude,
                    "lon": response.location.longitude
                }
            except Exception:
                pass

    reader.close()
    return df
```

### Step 2: Detect Impossible Travel Anomalies

Identify logins from geographically impossible locations:

```python
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  # Earth's radius in kilometers

    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 = []

    # Sort by user and timestamp
    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]

            # Skip if location data is missing
            if not prev.get("location") or not curr.get("location"):
                continue
            if not prev["location"].get("lat") or not curr["location"].get("lat"):
                continue

            # Calculate distance and time delta
            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

            # Flag if required speed exceeds maximum realistic travel
            if required_speed > max_speed_kmh and distance_km > 100:
                alerts.append({
                    "alert_type": "IMPOSSIBLE_TRAVEL",
                    "severity": "HIGH",