Imported from mukul975/anthropic-cybersecurity-skills (skills/performing-malware-ioc-extraction/SKILL.md). Install upstream with npx skills add mukul975/anthropic-cybersecurity-skills --skill performing-malware-ioc-extraction. Copyright stays with the author (Apache-2.0).

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

• 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

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

• Network Activity: DNS queries, HTTP requests, TCP/UDP connections, SSL certificates
• File System: Created/modified/deleted files and directories
• Registry: Created/modified registry keys and values
• Process: Spawned processes, injected processes, service creation
• Behavioral: API calls, mutex creation, scheduled tasks, persistence mechanisms

YARA Rules

YARA is a pattern-matching tool for identifying and classifying malware. Rules consist of strings (text, hex, regex) and conditions that define matching logic. Rules can detect malware families, packers, exploit kits, and specific campaign tools.

Workflow

Step 1: Static Analysis - PE Parsing and Hash Generation

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

Step 2: String Extraction and IOC Pattern Matching

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}

Step 3: YARA Rule Scanning

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
        )
}
"""

Step 4: Generate STIX 2.1 Indicators

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

Validation Criteria

• PE file parsed successfully with hashes, imports, and section analysis
• String extraction identifies network IOCs (IPs, domains, URLs)
• YARA rules match against known malware characteristics
• STIX 2.1 bundle contains valid Indicator and Malware objects
• Private IP ranges and benign strings filtered from IOC output
• IOCs are actionable for blocking and detection rule creation

References

• pefile Documentation
• YARA Documentation
• MITRE ATT&CK Software
• VirusTotal API
• CAPE Sandbox
• MalwareBazaar