detecting-rootkit-activity

name	detecting-rootkit-activity
description	Detects rootkit presence on compromised systems by identifying hidden processes, hooked system calls, modified kernel structures, hidden files, and covert network connections using memory forensics, cross-view detection, and integrity checking techniques. Activates for requests involving rootkit detection, hidden process discovery, kernel integrity checking, or system call hook analysis. . Use when working with detecting rootkit activity.
domain	cybersecurity
tags	["malware","rootkit","detection","kernel-analysis","memory-forensics"]
subdomain	malware-analysis
version	1.0.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"]

Detecting Rootkit Activity

Overview

Cybersecurity skill for detecting rootkit activity. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting rootkit activity"

• "Detects rootkit presence on compromised systems by identifying hidden processes,"

• System shows signs of compromise but standard tools (Task Manager, netstat) show nothing abnormal

• Antivirus/EDR detects rootkit signatures but cannot identify the specific hiding mechanism

• Memory forensics reveals discrepancies between kernel data structures and user-mode tool output

• Investigating a persistent threat that survives remediation attempts and system reboots

• Validating system integrity after a suspected kernel-level compromise

Do not use as a first-line detection method; start with standard malware triage and escalate to rootkit analysis when hiding behavior is suspected.

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

• Volatility 3 for memory forensics and kernel structure analysis
• GMER or Rootkit Revealer (Windows) for live system scanning
• rkhunter and chkrootkit (Linux) for filesystem and process integrity checks
• Sysinternals tools (Process Explorer, Autoruns, RootkitRevealer) for Windows analysis
• Memory dump from the suspected system (WinPmem, LiME)
• Clean baseline of the OS for comparison (known-good kernel module hashes)

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