Install this skill

npx skills add mukul975/Anthropic-Cybersecurity-Skills

Framework mappings

MITRE ATT&CK

NIST CSF 2.0

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

Source materials

References and resources

Everything below is rendered for inspection. Script files are read-only and never run.

References 3

api-reference.md1.7 KB

API Reference — Performing Malware IOC Extraction

Libraries Used

re: Regex patterns for 16 IOC types including defanged indicators
hashlib: MD5, SHA1, SHA256 file hashing
pathlib: File reading (text and binary)

CLI Interface

python agent.py text --file threat_report.txt
python agent.py hash --file malware.exe
python agent.py strings --file malware.exe [--min-length 6]
python agent.py report --file malware.exe [--output iocs.json]

Core Functions

`extract_iocs_from_text(text)` — Extract IOCs with defanging support

Handles defanged indicators: [.] -> ., hxxp -> http. Filters private IPs.

`extract_from_file(file_path)` — Extract IOCs from text/report files

`hash_file(file_path)` — Calculate MD5/SHA1/SHA256 hashes

`extract_strings(file_path, min_length)` — Binary string extraction

Extracts ASCII and wide (UTF-16LE) strings. Identifies suspicious API calls and keywords.

`generate_ioc_report(file_path, output)` — Full analysis report

IOC Pattern Types (16)

Type	Example
ipv4	192.168.1.1 (private filtered)
domain	evil.example.com
url	https://malware.example.com/payload
md5/sha1/sha256	File hashes
cve	CVE-2024-12345
registry_key	HKLM\Software...
file_path_windows	C:\Windows\Temp\mal.exe
mutex	Global\MutexName
mitre_technique	T1059.001
bitcoin_addr	Bitcoin wallet address
user_agent	Mozilla/5.0 strings

Suspicious String Keywords

CreateRemoteThread, VirtualAlloc, WriteProcessMemory, LoadLibrary, GetProcAddress, WinExec, ShellExecute, powershell, cmd.exe

Dependencies

No external packages — Python standard library only.

standards.md2.9 KB

Standards and Frameworks Reference

IOC Types and Classification

File-Based IOCs

Type	Description	Example
MD5	128-bit hash	d41d8cd98f00b204e9800998ecf8427e
SHA-1	160-bit hash	da39a3ee5e6b4b0d3255bfef95601890afd80709
SHA-256	256-bit hash	e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
Imphash	Import hash	PE import table hash for family grouping
SSDeep	Fuzzy hash	Context-triggered piecewise hash for similarity
TLSH	Trend Micro LSH	Locality-sensitive hash for near-duplicate detection

Network IOCs

Type	Description	Example
IPv4 Address	C2 server IP	192.0.2.1
Domain	C2 domain	malware-c2.example.com
URL	Full URL path	https://evil.com/payload.exe
JA3/JA3S	TLS fingerprint	Client/server TLS handshake hash
JARM	TLS server fingerprint	Active TLS server scanning fingerprint
User-Agent	HTTP User-Agent	Custom UA strings in beacons

Host-Based IOCs

Type	Description	Example
Mutex	Named mutex	Global{GUID}
Registry Key	Registry modification	HKLM\SOFTWARE...\Run
Scheduled Task	Persistence task	schtasks /create ...
Service Name	Malicious service	Malicious service installation
Named Pipe	IPC mechanism	\.\pipe\name
PDB Path	Debug path	C:\Users\dev\project.pdb

STIX 2.1 Indicator Patterns

Pattern Syntax

[file:hashes.'SHA-256' = 'abc123...']
[ipv4-addr:value = '1.2.3.4']
[domain-name:value = 'evil.com']
[url:value = 'https://evil.com/payload']
[file:name = 'malware.exe']
[email-addr:value = 'attacker@evil.com']
[network-traffic:dst_ref.type = 'ipv4-addr' AND network-traffic:dst_port = 443]

YARA Rule Structure

rule RuleName {
    meta:
        author = "Analyst"
        description = "Detection rule"
        reference = "URL"
        date = "YYYY-MM-DD"
        hash = "SHA256"
        tlp = "white"
    strings:
        $text = "string" ascii wide nocase
        $hex = { 4D 5A 90 00 }
        $regex = /pattern[0-9]+/
    condition:
        uint16(0) == 0x5A4D and filesize < 5MB and any of them
}

PE File Format

DOS Header: MZ signature (0x5A4D)
PE Header: PE signature, machine type, timestamp
Optional Header: Entry point, image base, subsystem
Section Table: .text, .data, .rdata, .rsrc, .reloc
Import Table: DLLs and functions used
Export Table: Functions exported (DLLs)
Resource Table: Embedded resources (icons, strings, configs)

References

workflows.md4.0 KB

Malware IOC Extraction Workflows

Workflow 1: Static Analysis Pipeline

[Malware Sample] --> [Hash Generation] --> [PE Parsing] --> [String Extraction] --> [IOC Filtering]
                                                                                        |
                                                                                        v
                                                                               [YARA Scanning]
                                                                                        |
                                                                                        v
                                                                               [STIX Bundle]

Steps:

Sample Acquisition: Obtain sample from MalwareBazaar, VirusTotal, or incident response
Hash Calculation: Generate MD5, SHA-1, SHA-256, imphash, ssdeep hashes
PE Analysis: Parse headers, sections, imports, exports, resources, timestamps
String Extraction: Extract ASCII/Unicode strings, apply IOC regex patterns
IOC Filtering: Remove false positives (private IPs, common DLLs, benign domains)
YARA Classification: Scan with community and custom YARA rules
Output: Generate STIX 2.1 bundle with extracted indicators

Workflow 2: Dynamic Analysis Pipeline

[Malware Sample] --> [Sandbox Submission] --> [Detonation] --> [Artifact Collection]
                                                                       |
                                                          +------------+------------+
                                                          |            |            |
                                                          v            v            v
                                                    [Network]    [File Sys]   [Registry]
                                                    [PCAPs]      [Changes]    [Changes]
                                                          |            |            |
                                                          +------------+------------+
                                                                       |
                                                                       v
                                                              [IOC Consolidation]

Steps:

Sandbox Setup: Configure isolated VM with network monitoring
Sample Submission: Submit to CAPE/Cuckoo sandbox with execution parameters
Execution Monitoring: Monitor for 3-5 minutes of runtime behavior
Network Capture: Extract DNS queries, HTTP/HTTPS traffic, raw connections
File System Analysis: Identify created, modified, and deleted files
Registry Analysis: Capture registry key changes for persistence indicators
Process Analysis: Document spawned processes, injections, privilege escalation
Consolidation: Merge static and dynamic IOCs into unified report

Workflow 3: Automated IOC Pipeline

[Feed/Alert] --> [Auto-Download] --> [Static Analysis] --> [Sandbox] --> [Enrichment] --> [Share]
                                                                              |
                                                                              v
                                                                     [VirusTotal Check]
                                                                              |
                                                                              v
                                                                     [MISP/OpenCTI Upload]

Steps:

Trigger: New sample from malware feed, email gateway, or EDR alert
Download: Retrieve sample securely to analysis infrastructure
Static Scan: Automated PE parsing, string extraction, YARA scanning
Dynamic Analysis: Submit to sandbox for behavioral analysis
Enrichment: Check hashes against VirusTotal, cross-reference with TI platforms
Deduplication: Remove already-known IOCs from output
Sharing: Upload new IOCs to MISP/OpenCTI for team consumption

Scripts 2

agent.py6.0 KB

Display-only source. This catalog never executes bundled scripts.

#!/usr/bin/env python3
"""Agent for performing malware IOC extraction from files, reports, and samples."""

import json
import argparse
import re
import hashlib
from pathlib import Path


IOC_PATTERNS = {
    "ipv4": re.compile(r"\b(?:(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)\b"),
    "ipv6": re.compile(r"\b(?:[0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}\b"),
    "domain": re.compile(r"\b(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+(?:com|net|org|io|ru|cn|xyz|top|info|biz|cc|tk|ml|ga|cf|gq|pw)\b"),
    "url": re.compile(r"https?://[^\s<>\"'\)]+"),
    "email": re.compile(r"\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b"),
    "md5": re.compile(r"\b[a-f0-9]{32}\b"),
    "sha1": re.compile(r"\b[a-f0-9]{40}\b"),
    "sha256": re.compile(r"\b[a-f0-9]{64}\b"),
    "cve": re.compile(r"CVE-\d{4}-\d{4,7}", re.I),
    "registry_key": re.compile(r"(?:HKLM|HKCU|HKCR|HKU|HKCC)\\[^\s\"']+"),
    "file_path_windows": re.compile(r"[A-Z]:\\(?:[^\s\\\"]+\\)*[^\s\\\"]+\.\w{1,5}"),
    "file_path_unix": re.compile(r"/(?:tmp|var|etc|usr|home|opt|bin|sbin)/[^\s\"']+"),
    "mutex": re.compile(r"(?:Global|Local)\\[^\s\"']+"),
    "bitcoin_addr": re.compile(r"\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b"),
    "mitre_technique": re.compile(r"T\d{4}(?:\.\d{3})?"),
    "user_agent": re.compile(r"Mozilla/5\.0[^\n\"]{20,200}"),
}

DEFANGED_PATTERNS = {
    "ip_defanged": (re.compile(r"\b\d+\[\.\]\d+\[\.\]\d+\[\.\]\d+\b"), lambda m: m.group().replace("[.]", ".")),
    "url_defanged": (re.compile(r"hxxps?://[^\s]+"), lambda m: m.group().replace("hxxp", "http")),
    "domain_defanged": (re.compile(r"\b\S+\[\.\]\S+\b"), lambda m: m.group().replace("[.]", ".")),
}


def extract_iocs_from_text(text):
    """Extract all IOC types from raw text."""
    refanged = text
    for name, (pattern, fixer) in DEFANGED_PATTERNS.items():
        refanged = pattern.sub(fixer, refanged)
    extracted = {}
    for ioc_type, pattern in IOC_PATTERNS.items():
        matches = list(set(pattern.findall(refanged)))
        if matches:
            extracted[ioc_type] = sorted(matches)[:200]
    private_ip = re.compile(r"^(?:10\.|172\.(?:1[6-9]|2\d|3[01])\.|192\.168\.|127\.)")
    if "ipv4" in extracted:
        extracted["ipv4"] = [ip for ip in extracted["ipv4"] if not private_ip.match(ip)]
    return extracted


def extract_from_file(file_path):
    """Extract IOCs from a file (text, PDF text, or report)."""
    content = Path(file_path).read_text(encoding="utf-8", errors="replace")
    iocs = extract_iocs_from_text(content)
    total = sum(len(v) for v in iocs.values())
    return {
        "source": file_path, "total_iocs": total,
        "by_type": {k: len(v) for k, v in iocs.items()},
        "indicators": iocs,
    }


def hash_file(file_path):
    """Calculate file hashes for malware sample identification."""
    data = Path(file_path).read_bytes()
    return {
        "file": file_path,
        "size_bytes": len(data),
        "md5": hashlib.md5(data).hexdigest(),
        "sha1": hashlib.sha1(data).hexdigest(),
        "sha256": hashlib.sha256(data).hexdigest(),
    }


def extract_strings(file_path, min_length=6):
    """Extract printable strings from binary file."""
    data = Path(file_path).read_bytes()
    ascii_strings = re.findall(rb"[\x20-\x7e]{%d,}" % min_length, data)
    wide_strings = re.findall(rb"(?:[\x20-\x7e]\x00){%d,}" % min_length, data)
    all_strings = [s.decode("ascii", errors="replace") for s in ascii_strings]
    all_strings += [s.decode("utf-16-le", errors="replace") for s in wide_strings]
    iocs = extract_iocs_from_text("\n".join(all_strings))
    suspicious = []
    suspicious_kw = ["http", "socket", "connect", "download", "upload", "exec", "cmd.exe",
                     "powershell", "reg add", "CreateRemoteThread", "VirtualAlloc", "WriteProcessMemory",
                     "LoadLibrary", "GetProcAddress", "WinExec", "ShellExecute"]
    for s in all_strings:
        if any(kw.lower() in s.lower() for kw in suspicious_kw):
            suspicious.append(s[:200])
    return {
        "file": file_path, "total_strings": len(all_strings),
        "suspicious_strings": suspicious[:30],
        "extracted_iocs": {k: len(v) for k, v in iocs.items()},
        "ioc_details": iocs,
    }


def generate_ioc_report(file_path, output=None):
    """Generate comprehensive IOC extraction report."""
    hashes = hash_file(file_path)
    strings = extract_strings(file_path)
    report = {
        "generated": datetime.utcnow().isoformat() if "datetime" in dir() else "",
        "file_info": hashes,
        "strings_analysis": {
            "total": strings["total_strings"],
            "suspicious": strings["suspicious_strings"],
        },
        "extracted_iocs": strings["ioc_details"],
        "ioc_summary": strings["extracted_iocs"],
    }
    if output:
        with open(output, "w") as f:
            json.dump(report, f, indent=2)
    return report


def main():
    parser = argparse.ArgumentParser(description="Malware IOC Extraction Agent")
    sub = parser.add_subparsers(dest="command")
    t = sub.add_parser("text", help="Extract IOCs from text/report file")
    t.add_argument("--file", required=True)
    h = sub.add_parser("hash", help="Calculate file hashes")
    h.add_argument("--file", required=True)
    s = sub.add_parser("strings", help="Extract strings and IOCs from binary")
    s.add_argument("--file", required=True)
    s.add_argument("--min-length", type=int, default=6)
    r = sub.add_parser("report", help="Generate full IOC report")
    r.add_argument("--file", required=True)
    r.add_argument("--output", help="Output JSON file")
    args = parser.parse_args()
    if args.command == "text":
        result = extract_from_file(args.file)
    elif args.command == "hash":
        result = hash_file(args.file)
    elif args.command == "strings":
        result = extract_strings(args.file, args.min_length)
    elif args.command == "report":
        result = generate_ioc_report(args.file, args.output)
    else:
        parser.print_help()
        return
    print(json.dumps(result, indent=2, default=str))


if __name__ == "__main__":
    main()

process.py16.2 KB

Display-only source. This catalog never executes bundled scripts.

#!/usr/bin/env python3
"""
Malware IOC Extraction Script

Performs static analysis on PE files to extract IOCs:
- File hash generation (MD5, SHA-1, SHA-256, imphash)
- PE header parsing and section analysis
- String extraction with IOC pattern matching
- YARA rule scanning
- STIX 2.1 bundle generation

Requirements:
    pip install pefile yara-python stix2 requests

Usage:
    python process.py --file malware.exe --output iocs.json
    python process.py --file malware.exe --yara-rules rules/ --stix-output bundle.json
    python process.py --file malware.exe --vt-check --vt-key YOUR_KEY
"""

import argparse
import hashlib
import json
import os
import re
import sys
from datetime import datetime
from typing import Optional

try:
    import pefile
except ImportError:
    pefile = None

try:
    import yara
except ImportError:
    yara = None


class MalwareIOCExtractor:
    """Extract IOCs from malware samples via static analysis."""

    def __init__(self, filepath: str):
        self.filepath = filepath
        self.filename = os.path.basename(filepath)

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

        self.hashes = self._calculate_hashes()
        self.iocs = {
            "file": {"name": self.filename, "size": len(self.data)},
            "hashes": self.hashes,
            "pe_info": {},
            "network_iocs": {},
            "host_iocs": {},
            "yara_matches": [],
            "suspicious_strings": [],
        }

    def _calculate_hashes(self) -> dict:
        return {
            "md5": hashlib.md5(self.data).hexdigest(),
            "sha1": hashlib.sha1(self.data).hexdigest(),
            "sha256": hashlib.sha256(self.data).hexdigest(),
        }

    def analyze_pe(self):
        """Parse PE file structure and extract metadata IOCs."""
        if pefile is None:
            print("[-] pefile not installed, skipping PE analysis")
            return

        try:
            pe = pefile.PE(data=self.data)
        except pefile.PEFormatError:
            print("[-] Not a valid PE file")
            return

        self.iocs["hashes"]["imphash"] = pe.get_imphash()

        # Compilation timestamp
        timestamp = pe.FILE_HEADER.TimeDateStamp
        try:
            compile_time = datetime.utcfromtimestamp(timestamp).isoformat()
        except (OSError, ValueError):
            compile_time = f"invalid ({timestamp})"

        self.iocs["pe_info"] = {
            "compile_time": compile_time,
            "machine": hex(pe.FILE_HEADER.Machine),
            "is_dll": pe.is_dll(),
            "is_exe": pe.is_exe(),
            "entry_point": hex(pe.OPTIONAL_HEADER.AddressOfEntryPoint),
            "image_base": hex(pe.OPTIONAL_HEADER.ImageBase),
            "sections": [],
            "imports": [],
            "exports": [],
        }

        # Section analysis
        for section in pe.sections:
            name = section.Name.decode("utf-8", errors="ignore").strip("\x00")
            entropy = section.get_entropy()
            self.iocs["pe_info"]["sections"].append({
                "name": name,
                "virtual_size": section.Misc_VirtualSize,
                "raw_size": section.SizeOfRawData,
                "entropy": round(entropy, 2),
                "suspicious": entropy > 7.0,
                "md5": hashlib.md5(section.get_data()).hexdigest(),
            })

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

        # Suspicious API imports
        suspicious_apis = {
            "VirtualAlloc", "VirtualProtect", "CreateRemoteThread",
            "WriteProcessMemory", "NtUnmapViewOfSection", "IsDebuggerPresent",
            "GetProcAddress", "LoadLibraryA", "LoadLibraryW",
            "URLDownloadToFileA", "InternetOpenA", "HttpSendRequestA",
            "WinExec", "ShellExecuteA", "CreateProcessA",
            "RegSetValueExA", "CryptEncrypt", "CryptDecrypt",
        }

        found_suspicious = set()
        for imp_entry in self.iocs["pe_info"]["imports"]:
            for func in imp_entry["functions"]:
                if func in suspicious_apis:
                    found_suspicious.add(func)

        self.iocs["pe_info"]["suspicious_apis"] = sorted(found_suspicious)

        # Export table
        if hasattr(pe, "DIRECTORY_ENTRY_EXPORT"):
            for exp in pe.DIRECTORY_ENTRY_EXPORT.symbols:
                if exp.name:
                    self.iocs["pe_info"]["exports"].append(
                        exp.name.decode("utf-8", errors="ignore")
                    )

        pe.close()

    def extract_strings(self, min_length: int = 4):
        """Extract and classify strings from the binary."""
        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|"
                r"onion|bit|me|co|uk|de|fr|jp|kr|br)\b"
            ),
            "url": re.compile(r"https?://[^\s\"'<>\x00]{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)"
                r"\\[\\a-zA-Z0-9_ .{}\-]+"
            ),
            "filepath": re.compile(
                r"[A-Z]:\\(?:[^\\/:*?\"<>|\r\n\x00]+\\)*[^\\/:*?\"<>|\r\n\x00]+"
            ),
            "mutex": re.compile(r"(?:Global\\|Local\\)[a-zA-Z0-9_\-{}.]{4,}"),
            "useragent": re.compile(r"Mozilla/[45]\.0[^\"'\x00]{10,200}"),
            "pdb_path": re.compile(r"[A-Z]:\\[^\x00\"]{5,200}\.pdb"),
            "bitcoin_wallet": re.compile(
                r"\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b"
            ),
        }

        # Extract ASCII strings
        ascii_regex = re.compile(rb"[\x20-\x7e]{%d,}" % min_length)
        ascii_strings = [
            s.decode("ascii", errors="ignore")
            for s in ascii_regex.findall(self.data)
        ]

        # Extract Unicode strings
        unicode_regex = re.compile(
            rb"(?:[\x20-\x7e]\x00){%d,}" % min_length
        )
        unicode_strings = [
            s.decode("utf-16-le", errors="ignore")
            for s in unicode_regex.findall(self.data)
        ]

        all_strings = ascii_strings + unicode_strings

        network_iocs = {"ipv4": set(), "domain": set(), "url": set(), "email": set()}
        host_iocs = {"registry": set(), "filepath": set(), "mutex": set()}
        other = {"useragent": set(), "pdb_path": set(), "bitcoin_wallet": set()}

        for string in all_strings:
            for category, pattern in patterns.items():
                for match in pattern.findall(string):
                    if category in network_iocs:
                        network_iocs[category].add(match)
                    elif category in host_iocs:
                        host_iocs[category].add(match)
                    else:
                        other[category].add(match)

        # Filter private IPs
        private_prefixes = ("10.", "172.16.", "172.17.", "172.18.", "172.19.",
                           "172.20.", "172.21.", "172.22.", "172.23.", "172.24.",
                           "172.25.", "172.26.", "172.27.", "172.28.", "172.29.",
                           "172.30.", "172.31.", "192.168.", "127.", "0.", "255.")
        network_iocs["ipv4"] = {
            ip for ip in network_iocs["ipv4"]
            if not ip.startswith(private_prefixes)
        }

        # Filter common benign domains
        benign_domains = {
            "microsoft.com", "windows.com", "google.com", "w3.org",
            "xmlsoap.org", "openxmlformats.org", "schemas.microsoft.com",
        }
        network_iocs["domain"] = {
            d for d in network_iocs["domain"]
            if not any(d.endswith(b) for b in benign_domains)
        }

        self.iocs["network_iocs"] = {k: sorted(v) for k, v in network_iocs.items() if v}
        self.iocs["host_iocs"] = {k: sorted(v) for k, v in host_iocs.items() if v}
        self.iocs["suspicious_strings"] = {k: sorted(v) for k, v in other.items() if v}

    def scan_yara(self, rules_path: str):
        """Scan with YARA rules."""
        if yara is None:
            print("[-] yara-python not installed, skipping YARA scan")
            return

        try:
            if os.path.isdir(rules_path):
                rule_files = {}
                for f in os.listdir(rules_path):
                    if f.endswith((".yar", ".yara")):
                        rule_files[f] = os.path.join(rules_path, f)
                rules = yara.compile(filepaths=rule_files)
            else:
                rules = yara.compile(filepath=rules_path)

            matches = rules.match(data=self.data)

            for match in matches:
                self.iocs["yara_matches"].append({
                    "rule": match.rule,
                    "tags": match.tags,
                    "meta": match.meta,
                    "string_count": len(match.strings),
                })
                print(f"[+] YARA match: {match.rule} (tags: {match.tags})")

        except yara.Error as e:
            print(f"[-] YARA error: {e}")

    def check_virustotal(self, api_key: str) -> Optional[dict]:
        """Check file hash against VirusTotal."""
        import requests

        sha256 = self.hashes["sha256"]
        resp = requests.get(
            f"https://www.virustotal.com/api/v3/files/{sha256}",
            headers={"x-apikey": api_key},
            timeout=30,
        )

        if resp.status_code == 200:
            data = resp.json().get("data", {}).get("attributes", {})
            stats = data.get("last_analysis_stats", {})
            vt_result = {
                "malicious": stats.get("malicious", 0),
                "suspicious": stats.get("suspicious", 0),
                "undetected": stats.get("undetected", 0),
                "total": sum(stats.values()),
                "popular_threat_name": data.get(
                    "popular_threat_classification", {}
                ).get("suggested_threat_label", ""),
                "tags": data.get("tags", []),
                "type_description": data.get("type_description", ""),
                "names": data.get("names", [])[:5],
            }
            self.iocs["virustotal"] = vt_result
            print(
                f"[+] VT: {vt_result['malicious']}/{vt_result['total']} "
                f"detections - {vt_result['popular_threat_name']}"
            )
            return vt_result
        elif resp.status_code == 404:
            print(f"[!] Hash not found on VirusTotal: {sha256}")
        else:
            print(f"[-] VT API error: {resp.status_code}")
        return None

    def generate_stix_bundle(self) -> dict:
        """Generate STIX 2.1 bundle from extracted IOCs."""
        from stix2 import Bundle, Indicator, Malware, Relationship

        objects = []

        malware_obj = Malware(
            name=self.filename,
            is_family=False,
            malware_types=["unknown"],
            description=(
                f"SHA256: {self.hashes['sha256']}\n"
                f"MD5: {self.hashes['md5']}"
            ),
            allow_custom=True,
        )
        objects.append(malware_obj)

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

        # Network indicators
        for ip in self.iocs.get("network_iocs", {}).get("ipv4", []):
            ind = Indicator(
                name=f"C2 IP: {ip}",
                pattern=f"[ipv4-addr:value = '{ip}']",
                pattern_type="stix",
                valid_from=datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ"),
                indicator_types=["malicious-activity"],
                allow_custom=True,
            )
            objects.append(ind)
            objects.append(Relationship(
                relationship_type="indicates",
                source_ref=ind.id,
                target_ref=malware_obj.id,
            ))

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

        bundle = Bundle(objects=objects, allow_custom=True)
        return json.loads(bundle.serialize())

    def get_report(self) -> dict:
        """Get complete IOC extraction report."""
        return self.iocs


def main():
    parser = argparse.ArgumentParser(description="Malware IOC Extraction Tool")
    parser.add_argument("--file", required=True, help="Path to malware sample")
    parser.add_argument("--output", default="iocs.json", help="Output IOC file")
    parser.add_argument("--yara-rules", help="YARA rules file or directory")
    parser.add_argument("--vt-check", action="store_true", help="Check VirusTotal")
    parser.add_argument("--vt-key", help="VirusTotal API key")
    parser.add_argument("--stix-output", help="Output STIX 2.1 bundle file")
    parser.add_argument(
        "--min-string-length", type=int, default=4,
        help="Minimum string length for extraction",
    )

    args = parser.parse_args()

    if not os.path.isfile(args.file):
        print(f"[-] File not found: {args.file}")
        sys.exit(1)

    print(f"[*] Analyzing: {args.file}")
    extractor = MalwareIOCExtractor(args.file)

    print("[*] Calculating hashes...")
    print(f"  MD5:    {extractor.hashes['md5']}")
    print(f"  SHA1:   {extractor.hashes['sha1']}")
    print(f"  SHA256: {extractor.hashes['sha256']}")

    print("[*] Parsing PE structure...")
    extractor.analyze_pe()

    print("[*] Extracting strings and IOC patterns...")
    extractor.extract_strings(min_length=args.min_string_length)

    if args.yara_rules:
        print(f"[*] Scanning with YARA rules: {args.yara_rules}")
        extractor.scan_yara(args.yara_rules)

    if args.vt_check and args.vt_key:
        print("[*] Checking VirusTotal...")
        extractor.check_virustotal(args.vt_key)

    report = extractor.get_report()
    with open(args.output, "w") as f:
        json.dump(report, f, indent=2, default=str)
    print(f"[+] IOC report saved to {args.output}")

    if args.stix_output:
        print("[*] Generating STIX 2.1 bundle...")
        bundle = extractor.generate_stix_bundle()
        with open(args.stix_output, "w") as f:
            json.dump(bundle, f, indent=2)
        print(f"[+] STIX bundle saved to {args.stix_output}")

    # Print summary
    net = report.get("network_iocs", {})
    host = report.get("host_iocs", {})
    print(f"\n=== IOC Summary ===")
    print(f"  IPs: {len(net.get('ipv4', []))}")
    print(f"  Domains: {len(net.get('domain', []))}")
    print(f"  URLs: {len(net.get('url', []))}")
    print(f"  Registry keys: {len(host.get('registry', []))}")
    print(f"  File paths: {len(host.get('filepath', []))}")
    print(f"  YARA matches: {len(report.get('yara_matches', []))}")


if __name__ == "__main__":
    main()

Assets 1

template.mdtext/markdown · 2.2 KB

Keep exploring

MITRE ATT&CK

NIST CSF 2.0

Overview

When to Use

Prerequisites

Key Concepts

Static Analysis IOCs

Dynamic Analysis IOCs

YARA Rules

Workflow

Step 1: Static Analysis - PE Parsing and Hash Generation

Step 2: String Extraction and IOC Pattern Matching

Step 3: YARA Rule Scanning

Step 4: Generate STIX 2.1 Indicators

Validation Criteria

References

References and resources

References 3

API Reference — Performing Malware IOC Extraction

Libraries Used

CLI Interface

Core Functions

extract_iocs_from_text(text) — Extract IOCs with defanging support

extract_from_file(file_path) — Extract IOCs from text/report files

hash_file(file_path) — Calculate MD5/SHA1/SHA256 hashes

extract_strings(file_path, min_length) — Binary string extraction

generate_ioc_report(file_path, output) — Full analysis report

IOC Pattern Types (16)

Suspicious String Keywords

Dependencies

Standards and Frameworks Reference

IOC Types and Classification

File-Based IOCs

Network IOCs

Host-Based IOCs

STIX 2.1 Indicator Patterns

Pattern Syntax

YARA Rule Structure

PE File Format

References

Malware IOC Extraction Workflows

Workflow 1: Static Analysis Pipeline

Steps:

Workflow 2: Dynamic Analysis Pipeline

Steps:

Workflow 3: Automated IOC Pipeline

Steps:

Scripts 2

Assets 1

Related skills

`extract_iocs_from_text(text)` — Extract IOCs with defanging support

`extract_from_file(file_path)` — Extract IOCs from text/report files

`hash_file(file_path)` — Calculate MD5/SHA1/SHA256 hashes

`extract_strings(file_path, min_length)` — Binary string extraction

`generate_ioc_report(file_path, output)` — Full analysis report