detecting-lateral-movement-with-splunk

name	detecting-lateral-movement-with-splunk
description	Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse. Use when detecting adversary lateral movement across networks using splunk spl queries.
domain	cybersecurity
tags	["threat-hunting","mitre-attack","lateral-movement","splunk","siem","proactive-detection","ta0008"]
subdomain	threat-hunting
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Application Protocol Command Analysis","Network Isolation","Network Traffic Analysis","Client-server Payload Profiling","Network Traffic Community Deviation"]
nist_csf	["DE.CM-01","DE.AE-02","DE.AE-07","ID.RA-05"]

Detecting Lateral Movement With Splunk

Overview

Cybersecurity skill for detecting lateral movement with splunk. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting lateral movement with splunk"

• "Detect adversary lateral movement across networks using Splunk SPL queries again"

• When hunting for adversary movement between compromised systems

• After detecting credential theft to trace subsequent lateral activity

• When investigating unusual authentication patterns across the network

• During incident response to scope the breadth of compromise

• When proactively hunting for TA0008 (Lateral Movement) techniques

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

• Splunk Enterprise or Splunk Cloud with Windows event data ingested
• Windows Security Event Logs forwarded (4624, 4625, 4648, 4672, 4768, 4769)
• Sysmon deployed for process creation and network connection data
• Network flow data or firewall logs for SMB/RDP/WinRM correlation
• Active Directory user and group membership reference data

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:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Define Detection Scope — Identify the specific lateral movement techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.