performing-malware-ioc-extraction

Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), regist. Use when working with performing malware ioc extraction.

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Repository: oyi77/1ai-skills
Last source activity: July 31, 2026 at 15:22
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name	performing-malware-ioc-extraction
description	Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), regist. Use when working with performing malware ioc extraction.
domain	cybersecurity
subdomain	threat-intelligence
tags	["threat-intelligence","cti","ioc","mitre-attack","stix","malware-analysis","yara","reverse-engineering"]
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"]

Performing Malware IOC Extraction

Overview

Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), registry modifications, mutex names, embedded strings, and behavioral artifacts. This skill covers static analysis with PE parsing and string extraction, dynamic analysis with sandbox detonation, automated IOC extraction using tools like YARA, and formatting results as STIX 2.1 indicators for sharing.

When to Use

Trigger phrases:

"performing malware ioc extraction"
"Malware IOC extraction is the process of analyzing malicious software to identif"
When conducting security assessments that involve performing malware ioc extraction
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing

Prerequisites

Python 3.9+ with pefile, yara-python, oletools, stix2 libraries
Access to malware analysis sandbox (Cuckoo, CAPE, Any.Run, Joe Sandbox)
VirusTotal API key for enrichment
Isolated analysis environment (VM or container)
Understanding of PE file format, common packing techniques
Familiarity with YARA rule syntax

Key Concepts

This section covers key concepts for performing malware ioc extraction.

Ensure all prerequisites are met before proceeding
Follow the documented workflow steps in sequence
Record results and any anomalies encountered during this phase

Static Analysis IOCs

File Hashes: MD5, SHA-1, SHA-256 of the sample and any dropped files
Import Hash (imphash): Hash of imported function table, groups malware families
Rich Header Hash: PE rich header hash for compiler fingerprinting
Strings: Embedded URLs, IP addresses, domain names, registry paths, mutex names
PE Metadata: Compilation timestamp, section names, resources, digital signatures
Embedded Artifacts: PDB paths, version info, certificate details

Dynamic Analysis IOCs

import pefile
import hashlib
import os

def analyze_pe(filepath):
    """Extract IOCs from a PE file through static analysis."""
    iocs = {"hashes": {}, "pe_info": {}, "strings": [], "imports": []}

    # Calculate file hashes
    with open(filepath, "rb") as f:
        data = f.read()
    iocs["hashes"]["md5"] = hashlib.md5(data).hexdigest()
    iocs["hashes"]["sha1"] = hashlib.sha1(data).hexdigest()
    iocs["hashes"]["sha256"] = hashlib.sha256(data).hexdigest()
    iocs["hashes"]["file_size"] = len(data)

    # Parse PE headers
    try:
        pe = pefile.PE(filepath)
        iocs["hashes"]["imphash"] = pe.get_imphash()
        iocs["pe_info"]["compilation_time"] = str(pe.FILE_HEADER.TimeDateStamp)
        iocs["pe_info"]["machine_type"] = hex(pe.FILE_HEADER.Machine)
        iocs["pe_info"]["subsystem"] = pe.OPTIONAL_HEADER.Subsystem

        # Extract sections
        iocs["pe_info"]["sections"] = []
        for section in pe.sections:
            iocs["pe_info"]["sections"].append({
                "name": section.Name.decode("utf-8", errors="ignore").strip("\x00"),
                "virtual_size": section.Misc_VirtualSize,
                "raw_size": section.SizeOfRawData,
                "entropy": section.get_entropy(),
                "md5": section.get_hash_md5(),
            })

        # Extract imports
        if hasattr(pe, "DIRECTORY_ENTRY_IMPORT"):
            for entry in pe.DIRECTORY_ENTRY_IMPORT:
                dll_name = entry.dll.decode("utf-8", errors="ignore")
                functions = [
                    imp.name.decode("utf-8", errors="ignore")
                    for imp in entry.imports
                    if imp.name
                ]
                iocs["imports"].append({"dll": dll_name, "functions": functions})

        # Check for suspicious characteristics
        iocs["pe_info"]["is_dll"] = pe.is_dll()
        iocs["pe_info"]["is_driver"] = pe.is_driver()
        iocs["pe_info"]["is_exe"] = pe.is_exe()

        # Version info
        if hasattr(pe, "VS_VERSIONINFO"):
            for entry in pe.FileInfo:
                for st in entry:
                    for item in st.entries.items():
                        key = item[0].decode("utf-8", errors="ignore")
                        val = item[1].decode("utf-8", errors="ignore")
                        iocs["pe_info"][f"version_{key}"] = val

        pe.close()

    except pefile.PEFormatError as e:
        iocs["pe_info"]["error"] = str(e)

    return iocs

import re

def extract_ioc_strings(filepath):
    """Extract IOC-relevant strings from binary file."""
    patterns = {
        "ipv4": re.compile(
            r"\b(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}"
            r"(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b"
        ),
        "domain": re.compile(
            r"\b(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+"
            r"(?:com|net|org|io|ru|cn|tk|xyz|top|info|biz|cc|ws|pw)\b"
        ),
        "url": re.compile(
            r"https?://[^\s\"'<>]{5,200}"
        ),
        "email": re.compile(
            r"\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b"
        ),
        "registry": re.compile(
            r"(?:HKEY_[A-Z_]+|HKLM|HKCU|HKU|HKCR|HKCC)"
            r"\\[\\a-zA-Z0-9_ .{}-]+"
        ),
        "filepath_windows": re.compile(
            r"[A-Z]:\\(?:[^\\/:*?\"<>|\r\n]+\\)*[^\\/:*?\"<>|\r\n]+"
        ),
        "mutex": re.compile(
            r"(?:Global\\|Local\\)[a-zA-Z0-9_\-{}.]{4,}"
        ),
        "useragent": re.compile(
            r"Mozilla/[45]\.0[^\"']{10,200}"
        ),
        "bitcoin": re.compile(
            r"\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b"
        ),
        "pdb_path": re.compile(
            r"[A-Z]:\\[^\"]{5,200}\.pdb"
        ),
    }

    with open(filepath, "rb") as f:
        data = f.read()

    # Extract ASCII strings (min length 4)
    ascii_strings = re.findall(rb"[\x20-\x7e]{4,}", data)
    # Extract Unicode strings
    unicode_strings = re.findall(
        rb"(?:[\x20-\x7e]\x00){4,}", data
    )

    all_strings = [s.decode("ascii", errors="ignore") for s in ascii_strings]
    all_strings += [
        s.decode("utf-16-le", errors="ignore") for s in unicode_strings
    ]

    extracted = {category: set() for category in patterns}

    for string in all_strings:
        for category, pattern in patterns.items():
            matches = pattern.findall(string)
            for match in matches:
                extracted[category].add(match)

    # Convert sets to sorted lists
    return {k: sorted(v) for k, v in extracted.items() if v}

import yara

def scan_with_yara(filepath, rules_path):
    """Scan file with YARA rules for malware classification."""
    rules = yara.compile(filepath=rules_path)
    matches = rules.match(filepath)

    results = []
    for match in matches:
        result = {
            "rule": match.rule,
            "namespace": match.namespace,
            "tags": match.tags,
            "meta": match.meta,
            "strings": [],
        }
        for offset, identifier, data in match.strings:
            result["strings"].append({
                "offset": hex(offset),
                "identifier": identifier,
                "data": data.hex() if len(data) < 100 else data[:100].hex() + "...",
            })
        results.append(result)

    return results


# Example YARA rule for common malware indicators
SAMPLE_YARA_RULE = """
rule Suspicious_Network_Indicators {
    meta:
        description = "Detects suspicious network-related strings"
        author = "CTI Analyst"
        severity = "medium"
    strings:
        $ua1 = "Mozilla/5.0" ascii
        $cmd1 = "cmd.exe /c" ascii nocase
        $ps1 = "powershell" ascii nocase
        $wget = "wget" ascii nocase
        $curl = "curl" ascii nocase
        $b64 = "base64" ascii nocase
        $reg1 = "HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run" ascii nocase
    condition:
        uint16(0) == 0x5A4D and
        (2 of ($ua1, $cmd1, $ps1, $wget, $curl, $b64)) or $reg1
}

rule Packed_Binary {
    meta:
        description = "Detects potentially packed binary"
        author = "CTI Analyst"
    condition:
        uint16(0) == 0x5A4D and
        for any section in pe.sections : (
            section.entropy >= 7.0
        )
}
"""

from stix2 import (
    Bundle, Indicator, Malware, Relationship,
    File as STIXFile, DomainName, IPv4Address,
    ObservedData,
)
from datetime import datetime

def create_stix_bundle(pe_iocs, string_iocs, yara_results, sample_name):
    """Create STIX 2.1 bundle from extracted IOCs."""
    objects = []

    # Create Malware SDO
    malware = Malware(
        name=sample_name,
        is_family=False,
        malware_types=["unknown"],
        description=f"Malware sample analyzed: {pe_iocs['hashes']['sha256']}",
        allow_custom=True,
    )
    objects.append(malware)

    # File hash indicator
    sha256 = pe_iocs["hashes"]["sha256"]
    hash_indicator = Indicator(
        name=f"Malware hash: {sha256[:16]}...",
        pattern=f"[file:hashes.'SHA-256' = '{sha256}']",
        pattern_type="stix",
        valid_from=datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ"),
        indicator_types=["malicious-activity"],
        allow_custom=True,
    )
    objects.append(hash_indicator)
    objects.append(Relationship(
        relationship_type="indicates",
        source_ref=hash_indicator.id,
        target_ref=malware.id,
    ))

    # Network indicators from strings
    for ip in string_iocs.get("ipv4", []):
        if not ip.startswith(("10.", "172.", "192.168.", "127.")):
            ip_indicator = Indicator(
                name=f"C2 IP: {ip}",
                pattern=f"[ipv4-addr:value = '{ip}']",
                pattern_type="stix",
                valid_from=datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ"),
                indicator_types=["malicious-activity"],
                allow_custom=True,
            )
            objects.append(ip_indicator)
            objects.append(Relationship(
                relationship_type="indicates",
                source_ref=ip_indicator.id,
                target_ref=malware.id,
            ))

    for domain in string_iocs.get("domain", []):
        domain_indicator = Indicator(
            name=f"C2 Domain: {domain}",
            pattern=f"[domain-name:value = '{domain}']",
            pattern_type="stix",
            valid_from=datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ"),
            indicator_types=["malicious-activity"],
            allow_custom=True,
        )
        objects.append(domain_indicator)
        objects.append(Relationship(
            relationship_type="indicates",
            source_ref=domain_indicator.id,
            target_ref=malware.id,
        ))

    bundle = Bundle(objects=objects, allow_custom=True)
    return bundle

Rationalization	Reality
"We are too small to be targeted"	Automated attacks target everyone. Size does not matter.
"Security slows us down"	A breach slows you down 100x more. Build security in from the start.
"We will fix it after launch"	Vulnerabilities in production are exploited within hours. Fix before deploy.