| name | extracting-iocs-from-malware-samples |
| description | 'Extracts indicators of compromise (IOCs) from malware samples including file hashes, network indicators (IPs, domains, URLs), host artifacts (file paths, registry keys, mutexes), and behavioral patterns for threat intelligence sharing and detection rule creation. Activates for requests involving IOC extraction, threat indicator harvesting, malware indicator collection, or building detection content from samples. ' |
| domain | cybersecurity |
| subdomain | malware-analysis |
| tags | - malware - IOC-extraction - threat-intelligence - indicators - detection |
| version | 1.0.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01 |
Extracting IOCs from Malware Samples
When to Use
- A malware analysis (static or dynamic) is complete and actionable indicators need to be extracted for defense teams
- Building blocklists for firewalls, proxies, and DNS sinkholes from analyzed samples
- Creating YARA rules, Snort/Suricata signatures, or SIEM detection content from malware artifacts
- Contributing to threat intelligence sharing platforms (MISP, OTX, ThreatConnect)
- Tracking malware campaigns by correlating IOCs across multiple samples
Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.
Prerequisites
- Python 3.8+ with
iocextract, pefile, yara-python libraries installed
- Completed malware analysis report (static analysis, dynamic analysis, or reverse engineering)
- Access to PCAP files, memory dumps, or sandbox reports from the analysis
- MISP instance or STIX/TAXII server for structured IOC sharing
- VirusTotal API key for IOC enrichment and validation
- CyberChef for decoding obfuscated indicators
Workflow
Step 1: Extract File-Based IOCs
Compute hashes and identify file metadata indicators:
md5sum malware_sample.exe
sha1sum malware_sample.exe
sha256sum malware_sample.exe
ssdeep malware_sample.exe
python3 -c "
import pefile
pe = pefile.PE('malware_sample.exe')
print(f'Imphash: {pe.get_imphash()}')
"
python3 -c "
import tlsh
with open('malware_sample.exe', 'rb') as f:
h = tlsh.hash(f.read())
print(f'TLSH: {h}')
"
python3 << 'PYEOF'
import pefile
import os
import hashlib
import datetime
pe = pefile.PE("malware_sample.exe")
print("FILE IOCs:")
with open("malware_sample.exe", "rb") as f:
data = f.read()
print(f" MD5: {hashlib.md5(data).hexdigest()}")
print(f" SHA-1: {hashlib.sha1(data).hexdigest()}")
print(f" SHA-256: {hashlib.sha256(data).hexdigest()}")
print(f" File Size: {len(data)} bytes")
ts = pe.FILE_HEADER.TimeDateStamp
print(f" Compile: {datetime.datetime.utcfromtimestamp(ts)} UTC")
print(f" Imphash: {pe.get_imphash()}")
PYEOF
Step 2: Extract Network IOCs
Pull network indicators from strings, PCAP, and sandbox reports:
import re
with open("malware_sample.exe", "rb") as f:
data = f.read()
ascii_strings = re.findall(b'[ -~]{4,}', data)
unicode_strings = re.findall(b'(?:[ -~]\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]
ip_pattern = re.compile(r'\b(?:(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\b')
ips = set()
for s in all_strings:
for ip in ip_pattern.findall(s):
octets = [int(o) for o in ip.split('.')]
if octets[0] not in [10, 127, 0] and not (octets[0] == 172 and 16 <= octets[1] <= 31) and not (octets[0] == 192 and octets[1] == 168):
ips.add(ip)
domain_pattern = re.compile(r'\b[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z]{2,})+\b')
domains = set()
for s in all_strings:
for d in domain_pattern.findall(s):
if not d.endswith(('.dll', '.exe', '.sys', '.com.au')):
domains.add(d)
url_pattern = re.compile(r'https?://[^\s<>"{}|\\^`\[\]]+')
urls = set()
for s in all_strings:
for u in url_pattern.findall(s):
urls.add(u)
print("NETWORK IOCs:")
print(f" IPs: {ips}")
print(f" Domains: {domains}")
print(f" URLs: {urls}")
Step 3: Extract Host-Based IOCs
Identify file paths, registry keys, mutexes, and services:
import json
with open("cuckoo_report.json") as f:
report = json.load(f)
print("HOST IOCs:")
print("\nFile Paths:")
for f in report["behavior"]["summary"].get("files", []):
if any(p in f.lower() for p in ["temp", "appdata", "system32", "programdata"]):
print(f" [DROPPED] {f}")
print("\nRegistry Keys:")
for key in report["behavior"]["summary"].get("write_keys", []):
if any(p in key.lower() for p in ["run", "service", "startup", "shell"]):
print(f" [PERSIST] {key}")
print("\nMutexes:")
for mutex in report["behavior"]["summary"].get("mutexes", []):
if mutex not in ["Local\\!IETld!Mutex", "RasPbFile"]:
print(f" [MUTEX] {mutex}")
print("\nServices:")
for svc in report["behavior"]["summary"].get("started_services", []):
print(f" [SERVICE] {svc}")
Step 4: Extract Network IOCs from PCAP
Parse network captures for additional indicators:
tshark -r capture.pcap -T fields -e dns.qry.name -Y "dns.flags.response == 0" | sort -u
tshark -r capture.pcap -T fields -e http.host -e http.request.uri -Y "http.request" | sort -u
tshark -r capture.pcap -T fields -e tls.handshake.extensions_server_name -Y "tls.handshake.type == 1" | sort -u
tshark -r capture.pcap -T fields -e tls.handshake.ja3 -Y "tls.handshake.type == 1" | sort -u
tshark -r capture.pcap -T fields -e ip.dst -Y "ip.src == 10.0.2.15" | sort -u
tshark -r capture.pcap -T fields -e http.user_agent -Y "http.user_agent" | sort -u
Step 5: Defang and Validate IOCs
Defang indicators for safe sharing and validate against threat intelligence:
def defang_ip(ip):
return ip.replace(".", "[.]")
def defang_url(url):
return url.replace("http", "hxxp").replace(".", "[.]")
def defang_domain(domain):
return domain.replace(".", "[.]")
import requests
VT_API_KEY = "your_api_key"
def check_vt_ip(ip):
resp = requests.get(f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
headers={"x-apikey": VT_API_KEY})
data = resp.json()
stats = data["data"]["attributes"]["last_analysis_stats"]
return stats["malicious"]
def check_vt_domain(domain):
resp = requests.get(f"https://www.virustotal.com/api/v3/domains/{domain}",
headers={"x-apikey": VT_API_KEY})
data = resp.json()
stats = data["data"]["attributes"]["last_analysis_stats"]
return stats["malicious"]
for ip in ips:
detections = check_vt_ip(ip)
print(f" {defang_ip(ip)} - VT: {detections} detections")
Step 6: Export IOCs in Standard Formats
Generate structured IOC outputs for sharing and ingestion:
from stix2 import Indicator, Bundle, Malware, Relationship
import datetime
indicators = []
indicators.append(Indicator(
name="Malware SHA-256 Hash",
pattern=f"[file:hashes.'SHA-256' = '{sha256_hash}']",
pattern_type="stix",
valid_from=datetime.datetime.now(datetime.timezone.utc),
labels=["malicious-activity"]
))
for ip in ips:
indicators.append(Indicator(
name=f"C2 IP Address {ip}",
pattern=f"[ipv4-addr:value = '{ip}']",
pattern_type="stix",
valid_from=datetime.datetime.now(datetime.timezone.utc),
labels=["malicious-activity"]
))
for domain in domains:
indicators.append(Indicator(
name=f"C2 Domain {domain}",
pattern=f"[domain-name:value = '{domain}']",
pattern_type="stix",
valid_from=datetime.datetime.now(datetime.timezone.utc),
labels=["malicious-activity"]
))
bundle = Bundle(objects=indicators)
with open("iocs_stix.json", "w") as f:
f.write(bundle.serialize(pretty=True))
import csv
with open("iocs.csv", "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(["type", "value", "context", "confidence"])
writer.writerow(["sha256", sha256_hash, "malware_sample", "high"])
for ip in ips:
writer.writerow(["ipv4", ip, "c2_server", "high"])
for domain in domains:
writer.writerow(["domain", domain, "c2_domain", "high"])
for url in urls:
writer.writerow(["url", url, "c2_url", "high"])
Key Concepts
| Term | Definition |
|---|
| IOC (Indicator of Compromise) | Forensic artifact observed in a network or system that indicates a potential intrusion: hashes, IPs, domains, file paths, registry keys |
| Defanging | Modifying IOCs to prevent accidental activation (e.g., replacing dots with [.] in URLs and IPs for safe sharing in reports) |
| Imphash | MD5 hash of the import table functions in a PE file; samples from the same malware family often share the same imphash |
| STIX/TAXII | Structured Threat Information Expression / Trusted Automated Exchange; standards for encoding and transmitting threat intelligence |
| JA3/JA3S | TLS client/server fingerprint based on ClientHello/ServerHello parameters; identifies specific malware families by their TLS implementation |
| Fuzzy Hashing (ssdeep) | Context-triggered piecewise hashing that identifies similar files even with minor modifications; useful for malware variant detection |
| MISP | Malware Information Sharing Platform; open-source threat intelligence platform for collecting, storing, and sharing IOCs |
Tools & Systems
- iocextract (Python): Automated IOC extraction library supporting IPs, URLs, domains, hashes, and YARA rules from text
- MISP: Open-source threat intelligence sharing platform for structured IOC management and distribution
- CyberChef: Web-based tool for decoding, decrypting, and transforming data useful for deobfuscating encoded IOCs
- tshark: Command-line network protocol analyzer for extracting network IOCs from PCAP files
- VirusTotal: Online service for validating and enriching IOCs with community detection results and threat intelligence
Common Scenarios
Scenario: Building a Comprehensive IOC Package from a Ransomware Sample
Context: A ransomware incident requires rapid IOC extraction for blocking across the enterprise while the full investigation continues. Multiple data sources are available: the sample binary, PCAP from network monitoring, and a Cuckoo sandbox report.
Approach:
- Compute all file hashes (MD5, SHA-1, SHA-256, imphash, ssdeep) for the ransomware binary and any dropped files
- Extract network IOCs from strings in the binary (hardcoded C2 addresses)
- Parse the PCAP for DNS queries, HTTP requests, and TLS SNI fields
- Extract host IOCs from the sandbox report (file paths, registry keys, mutexes, ransom note filenames)
- Validate all network IOCs against VirusTotal to confirm malicious status and check for known associations
- Defang all indicators and compile into STIX 2.1 format for sharing and CSV for SIEM ingestion
- Submit to MISP event for organizational and community sharing
Pitfalls:
- Including IP addresses of legitimate CDNs or cloud services without validating context (e.g., AWS IPs used for hosting, not inherently malicious)
- Not defanging URLs and IPs in reports, leading to accidental clicks or DNS resolution
- Extracting strings from packed binaries (IOCs from packed samples are unreliable; unpack first)
- Forgetting to include dropped file hashes (the initial dropper and the final payload are separate IOCs)
Output Format
IOC EXTRACTION REPORT
======================
Sample: ransomware.exe
Analysis Date: 2025-09-15
Analyst: [Name]
FILE INDICATORS
SHA-256: e3b0c44298fc1c149afbf4c8996fb924...
SHA-1: da39a3ee5e6b4b0d3255bfef95601890afd80709
MD5: d41d8cd98f00b204e9800998ecf8427e
Imphash: a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6
ssdeep: 3072:kJh3bN7fY+aUkJh3bN7fY+aU:kJh3R7aUkJh3R7aU
NETWORK INDICATORS
C2 IPs: 185.220.101[.]42, 91.215.85[.]17
C2 Domains: update.malicious[.]com, backup.evil[.]net
C2 URLs: hxxps://update.malicious[.]com/gate.php
hxxps://backup.evil[.]net/gate.php
JA3 Hash: a0e9f5d64349fb13191bc781f81f42e1
User-Agent: Mozilla/5.0 (compatible; MSIE 10.0)
HOST INDICATORS
File Paths: C:\Users\Public\svchost.exe
C:\Users\%USER%\AppData\Local\Temp\payload.dll
C:\Users\%USER%\Desktop\README_DECRYPT.txt
Registry Keys: HKCU\Software\Microsoft\Windows\CurrentVersion\Run\WindowsUpdate
Mutexes: Global\CryptLocker_2025_Q3
Services: FakeWindowsUpdate
CONFIDENCE ASSESSMENT
High Confidence: SHA-256, C2 IPs (validated via VT), Mutexes
Medium Confidence: Domains (could be compromised legitimate sites)
Low Confidence: User-Agent (common string, high false positive risk)
EXPORT FILES
stix_bundle.json - STIX 2.1 format for TIP ingestion
iocs.csv - Flat CSV for SIEM blocklist import
yara_rule.yar - YARA detection rule
