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skills/performing-malware-ioc-extraction/SKILL.md
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---
name: None
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
domain: cybersecurity
subdomain: threat-intelligence
tags: [threat-intelligence, cti, ioc, mitre-attack, stix, malware-analysis, yara, reverse-engineering]
version: "1.0"
author: mahipal
license: MIT
---
# 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.
## 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.
## Practical Steps
### Step 1: Static Analysis - PE Parsing and Hash Generation
```python
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
```python
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
```python
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
```python
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](https://github.com/erocarrera/pefile)
- [YARA Documentation](https://yara.readthedocs.io/)
- [MITRE ATT&CK Software](https://attack.mitre.org/software/)
- [VirusTotal API](https://docs.virustotal.com/)
- [CAPE Sandbox](https://github.com/kevoreilly/CAPEv2)
- [MalwareBazaar](https://bazaar.abuse.ch/)
skills/performing-malware-ioc-extraction/assets/template.md
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# Malware IOC Extraction Report Template
## Sample Information
| Field | Value |
|-------|-------|
| Filename | |
| File Size | |
| File Type | PE32/PE32+/ELF/Mach-O |
| MD5 | |
| SHA-1 | |
| SHA-256 | |
| Imphash | |
| SSDeep | |
| First Seen | |
## PE Analysis
| Attribute | Value |
|-----------|-------|
| Compile Time | |
| Entry Point | |
| Machine Type | |
| Subsystem | |
| Is DLL | |
| Digital Signature | Valid/Invalid/None |
### Sections
| Name | Virtual Size | Raw Size | Entropy | Suspicious |
|------|-------------|----------|---------|------------|
| .text | | | | |
| .data | | | | |
| .rsrc | | | | |
### Suspicious API Imports
| DLL | Function | Purpose |
|-----|----------|---------|
| kernel32.dll | VirtualAlloc | Memory allocation (code injection) |
| kernel32.dll | CreateRemoteThread | Process injection |
| wininet.dll | InternetOpenA | Network communication |
## Network IOCs
### IP Addresses
| IP | Context | Confidence |
|----|---------|-----------|
| | C2 Server | High/Med/Low |
### Domains
| Domain | Context | Confidence |
|--------|---------|-----------|
| | C2 Domain | High/Med/Low |
### URLs
| URL | Context | Confidence |
|-----|---------|-----------|
| | Payload Download | High/Med/Low |
## Host IOCs
### Registry Keys
| Key Path | Value | Purpose |
|----------|-------|---------|
| HKLM\...\Run | | Persistence |
### Mutexes
| Mutex Name | Purpose |
|-----------|---------|
| | Infection marker |
### File System Artifacts
| Path | Description |
|------|------------|
| | Dropped payload |
## YARA Matches
| Rule | Tags | Description |
|------|------|------------|
| | | |
## VirusTotal Results
| Metric | Value |
|--------|-------|
| Detection Ratio | X / Y |
| Threat Label | |
| First Submission | |
| Community Score | |
## MITRE ATT&CK Mapping
| Technique | Name | Evidence |
|-----------|------|----------|
| T1059.001 | PowerShell | Embedded PS commands |
| T1547.001 | Registry Run Keys | HKLM Run key modification |
| T1071.001 | Web Protocols | HTTP C2 communication |
## Recommendations
1. **Block**: Add network IOCs to firewall/proxy blocklists
2. **Detect**: Deploy YARA rules on endpoints and email gateways
3. **Hunt**: Search for host IOCs across the environment
4. **Share**: Upload IOCs to MISP with TLP classification
skills/performing-malware-ioc-extraction/references/standards.md
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# 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
- [STIX 2.1 Patterning](https://docs.oasis-open.org/cti/stix/v2.1/os/stix-v2.1-os.html#_e8slinrhxcc9)
- [YARA Documentation](https://yara.readthedocs.io/en/stable/)
- [PE Format Specification](https://learn.microsoft.com/en-us/windows/win32/debug/pe-format)
- [MalwareBazaar Database](https://bazaar.abuse.ch/)
skills/performing-malware-ioc-extraction/references/workflows.md
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# 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:
1. **Sample Acquisition**: Obtain sample from MalwareBazaar, VirusTotal, or incident response
2. **Hash Calculation**: Generate MD5, SHA-1, SHA-256, imphash, ssdeep hashes
3. **PE Analysis**: Parse headers, sections, imports, exports, resources, timestamps
4. **String Extraction**: Extract ASCII/Unicode strings, apply IOC regex patterns
5. **IOC Filtering**: Remove false positives (private IPs, common DLLs, benign domains)
6. **YARA Classification**: Scan with community and custom YARA rules
7. **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:
1. **Sandbox Setup**: Configure isolated VM with network monitoring
2. **Sample Submission**: Submit to CAPE/Cuckoo sandbox with execution parameters
3. **Execution Monitoring**: Monitor for 3-5 minutes of runtime behavior
4. **Network Capture**: Extract DNS queries, HTTP/HTTPS traffic, raw connections
5. **File System Analysis**: Identify created, modified, and deleted files
6. **Registry Analysis**: Capture registry key changes for persistence indicators
7. **Process Analysis**: Document spawned processes, injections, privilege escalation
8. **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:
1. **Trigger**: New sample from malware feed, email gateway, or EDR alert
2. **Download**: Retrieve sample securely to analysis infrastructure
3. **Static Scan**: Automated PE parsing, string extraction, YARA scanning
4. **Dynamic Analysis**: Submit to sandbox for behavioral analysis
5. **Enrichment**: Check hashes against VirusTotal, cross-reference with TI platforms
6. **Deduplication**: Remove already-known IOCs from output
7. **Sharing**: Upload new IOCs to MISP/OpenCTI for team consumption
skills/performing-malware-ioc-extraction/scripts/process.py
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#!/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()