performing-user-behavior-analytics

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
tags	["soc","ueba","user-behavior","insider-threat","anomaly-detection","splunk","baseline"]
subdomain	soc-operations
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"]

Performing User Behavior Analytics

Overview

Cybersecurity skill for performing user behavior analytics. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "performing user behavior analytics"
• "SOC teams need to detect compromised accounts through abnormal authentication pa"
• "Insider threat programs require behavioral monitoring beyond rule-based detectio"
• "Impossible travel or geographic anomalies indicate credential compromise"

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.

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

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

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
     {k: re.findall(v, text)  k, v  IOC_PATTERNS.items()}