performing-lateral-movement-detection

name	performing-lateral-movement-detection
description	Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI execution, RDP pivoting, and SMB-based spreading using SIEM correlation of Windows event logs, network flow data, and endpoint telemetry mapped to MITRE ATT&CK Lateral Movement (TA0008) techniques. . Use when working with performing lateral movement detection.
domain	cybersecurity
tags	["soc","lateral-movement","mitre-attack","pass-the-hash","psexec","wmi","rdp","smb","detection"]
subdomain	soc-operations
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Token Binding","Execution Isolation","Restore Access","Application Protocol Command Analysis","Process Termination"]
nist_csf	["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"]

Performing Lateral Movement Detection

Overview

Cybersecurity skill for performing lateral movement detection. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "performing lateral movement detection"
• "Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI executi"

Use this skill when:

• SOC teams need to detect attackers pivoting between systems after initial compromise
• Incident investigations require tracking an attacker's movement path through the network
• Detection engineering needs lateral movement rules mapped to ATT&CK TA0008 techniques
• Red/purple team exercises identify lateral movement detection gaps

Do not use for detecting initial access or external attacks — lateral movement detection focuses on internal host-to-host pivot activity.

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

• Windows Security Event Logs (EventCode 4624, 4625, 4648, 4672) from all endpoints and servers
• Sysmon deployed with process creation (EventCode 1), network connections (EventCode 3), and named pipe (EventCode 17/18)
• Network flow data (NetFlow/sFlow, Zeek connection logs) for internal traffic analysis
• SIEM with cross-source correlation capability
• Baseline of normal internal authentication patterns

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  IOC_PATTERNS.items()}