yanacuti1121 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,stix2libraries - 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