Build comprehensive threat actor profiles using open-source intelligence (OSINT) techniques to document adversary motivations, capabilities, infrastructure, and TTPs for proactive defense.
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Build comprehensive threat actor profiles using open-source intelligence (OSINT) techniques to document adversary motivations, capabilities, infrastructure, and TTPs for proactive defense.
Threat actor profiling using OSINT systematically gathers and analyzes publicly available information to build comprehensive profiles of adversary groups. This skill covers collecting intelligence from public sources (security vendor reports, paste sites, dark web forums, social media, code repositories), correlating indicators across platforms, mapping adversary infrastructure using tools like Maltego and SpiderFoot, and producing structured threat actor dossiers that inform defensive strategies and attribution assessments.
When to Use
When deploying or configuring building threat actor profile from osint capabilities in your environment
When establishing security controls aligned to compliance requirements
When building or improving security architecture for this domain
When conducting security assessments that require this implementation
Prerequisites
Python 3.9+ with shodan, requests, beautifulsoup4, maltego-trx, stix2 libraries
API keys: Shodan, VirusTotal, AlienVault OTX, PassiveTotal/RiskIQ
MITRE ATT&CK knowledge for TTP mapping
Understanding of STIX 2.1 Intrusion Set, Threat Actor, and Identity SDOs
Key Concepts
OSINT Sources for Threat Actor Profiling
Primary intelligence sources include vendor threat reports (Mandiant, CrowdStrike, Recorded Future, Talos), government advisories (CISA, NSA, FBI joint advisories), academic research papers, malware repositories (VirusTotal, MalwareBazaar, Malpedia), paste sites (Pastebin, GitHub Gists), code repositories, social media accounts, dark web forums, and certificate transparency logs.
Structured Analytical Techniques
Profiling uses the Diamond Model (adversary, infrastructure, capability, victim), Analysis of Competing Hypotheses (ACH) for attribution confidence, and MITRE ATT&CK mapping for TTP documentation. Link analysis tools like Maltego visualize relationships between indicators, infrastructure, and actors.
Profile Components
A complete threat actor profile includes: aliases and naming conventions across vendors, suspected origin and sponsorship, motivation (espionage, financial, hacktivism, disruption), targeted sectors and geographies, known campaigns and operations, TTPs mapped to ATT&CK, toolset and malware families, infrastructure patterns, and historical timeline.
Workflow
Step 1: Collect Intelligence from Multiple Sources
import requests
import json
from datetime import datetime
classOSINTCollector:
def__init__(self, vt_key=None, otx_key=None, shodan_key=None):
self.vt_key = vt_key
self.otx_key = otx_key
self.shodan_key = shodan_key
self.collected_data = {"sources": [], "indicators": [], "reports": []}
defsearch_alienvault_otx(self, actor_name):
"""Search AlienVault OTX for threat actor pulses."""
headers = {"X-OTX-API-KEY": self.otx_key}
url = f"https://otx.alienvault.com/api/v1/search/pulses?q={actor_name}&limit=20"
resp = requests.get(url, headers=headers)
if resp.status_code == 200:
data = resp.json()
pulses = data.get("results", [])
for pulse in pulses:
self.collected_data["reports"].append({
"source": "AlienVault OTX",
"title": pulse.get("name", ""),
"created": pulse.get("created", ""),
"description": pulse.get("description", "")[:500],
"tags": pulse.get("tags", []),
"indicators_count": len(pulse.get("indicators", [])),
"pulse_id": pulse.get("id", ""),
})
for ioc in pulse.get("indicators", []):
self.collected_data["indicators"].append({
"type": ioc.get("type", ""),
"value": ioc.get("indicator", ""),
"source": "OTX",
"pulse": pulse.get("name", ""),
})
print(f"[+] OTX: Found {len(pulses)} pulses for '{actor_name}'")
returnself.collected_data
defsearch_virustotal_collections(self, actor_name):
"""Search VirusTotal for threat actor collections."""
headers = {"x-apikey": self.vt_key}
url = "https://www.virustotal.com/api/v3/intelligence/search"
params = {"query": f"tag:{actor_name.lower().replace(' ', '-')}"}
resp = requests.get(url, headers=headers, params=params)
if resp.status_code == 200:
results = resp.json().get("data", [])
print(f"[+] VT: Found {len(results)} samples tagged '{actor_name}'")
return results
return []
defquery_shodan_infrastructure(self, indicators):
"""Query Shodan for infrastructure details on IPs."""
results = []
for ip in indicators:
url = f"https://api.shodan.io/shodan/host/{ip}?key={self.shodan_key}"
resp = requests.get(url)
if resp.status_code == 200:
data = resp.json()
results.append({
"ip": ip,
"org": data.get("org", ""),
"asn": data.get("asn", ""),
"country": data.get("country_code", ""),
"ports": data.get("ports", []),
"hostnames": data.get("hostnames", []),
"os": data.get("os", ""),
"last_update": data.get("last_update", ""),
})
print(f"[+] Shodan: Enriched {len(results)} IPs")
return results
collector = OSINTCollector(
vt_key="YOUR_VT_KEY",
otx_key="YOUR_OTX_KEY",
shodan_key="YOUR_SHODAN_KEY",
)
data = collector.search_alienvault_otx("APT29")
Step 2: Build Structured Threat Actor Profile
from stix2 import ThreatActor, IntrusionSet, Identity, Relationship, Bundle
from datetime import datetime
# Create STIX 2.1 Threat Actor profile
identity = Identity(
name="Cybersecurity Analyst",
identity_class="individual",
)
threat_actor = ThreatActor(
name="APT29",
description="APT29 (also known as Cozy Bear, Midnight Blizzard, NOBELIUM, The Dukes) ""is a Russian state-sponsored threat group attributed to Russia's Foreign ""Intelligence Service (SVR). Active since at least 2008, the group conducts ""cyber espionage targeting government, diplomatic, think tank, healthcare, ""and energy organizations primarily in NATO countries.",
aliases=["Cozy Bear", "Midnight Blizzard", "NOBELIUM", "The Dukes",
"Dark Halo", "UNC2452", "YTTRIUM", "Blue Kitsune", "Iron Ritual"],
roles=["agent"],
sophistication="strategic",
resource_level="government",
primary_motivation="organizational-gain",
secondary_motivations=["ideology"],
threat_actor_types=["nation-state"],
goals=["Intelligence collection on foreign governments",
"Long-term persistent access to high-value targets",
"Supply chain compromise for broad access"],
created_by_ref=identity.id,
)
intrusion_set = IntrusionSet(
name="APT29",
description="Intrusion set tracked as APT29, attributed to Russian SVR.",
aliases=["Cozy Bear", "Midnight Blizzard"],
first_seen="2008-01-01T00:00:00Z",
goals=["espionage"],
resource_level="government",
primary_motivation="organizational-gain",
)
relationship = Relationship(
relationship_type="attributed-to",
source_ref=intrusion_set.id,
target_ref=threat_actor.id,
)
bundle = Bundle(objects=[identity, threat_actor, intrusion_set, relationship])
withopen("apt29_profile.json", "w") as f:
f.write(bundle.serialize(pretty=True))
print("[+] STIX profile saved: apt29_profile.json")
Step 3: Map TTPs to MITRE ATT&CK
from attackcti import attack_client
lift = attack_client()
apt29_techs = lift.get_techniques_used_by_group("G0016")
profile_ttps = {
"initial_access": [],
"execution": [],
"persistence": [],
"defense_evasion": [],
"credential_access": [],
"lateral_movement": [],
"collection": [],
"c2": [],
"exfiltration": [],
}
tactic_mapping = {
"initial-access": "initial_access",
"execution": "execution",
"persistence": "persistence",
"defense-evasion": "defense_evasion",
"credential-access": "credential_access",
"lateral-movement": "lateral_movement",
"collection": "collection",
"command-and-control": "c2",
"exfiltration": "exfiltration",
}
for tech in apt29_techs:
tech_id = ""for ref in tech.get("external_references", []):
if ref.get("source_name") == "mitre-attack":
tech_id = ref.get("external_id", "")
breakfor phase in tech.get("kill_chain_phases", []):
tactic = phase.get("phase_name", "")
key = tactic_mapping.get(tactic)
if key:
profile_ttps[key].append({
"id": tech_id,
"name": tech.get("name", ""),
"description": tech.get("description", "")[:200],
})
print("=== APT29 TTP Profile ===")
for tactic, techs in profile_ttps.items():
if techs:
print(f"\n{tactic.upper()} ({len(techs)} techniques):")
for t in techs[:5]:
print(f" {t['id']}: {t['name']}")
Step 4: Correlate Infrastructure with SpiderFoot
import subprocess
import json
defrun_spiderfoot_scan(target, scan_name="actor_recon"):
"""Run SpiderFoot scan against target domain or IP."""
cmd = [
"python3", "-m", "spiderfoot", "-s", target,
"-m", "sfp_dns,sfp_whois,sfp_shodan,sfp_virustotal,sfp_certspotter",
"-o", "json", "-q",
]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
if result.returncode == 0:
findings = json.loads(result.stdout) if result.stdout else []
print(f"[+] SpiderFoot: {len(findings)} findings for {target}")
return findings
return []
defcorrelate_infrastructure(indicators):
"""Find relationships between infrastructure indicators."""
ip_to_domains = {}
domain_to_ips = {}
registrar_patterns = {}
for indicator in indicators:
ioc_type = indicator.get("type", "")
value = indicator.get("value", "")
if ioc_type == "IP_ADDRESS":
if value notin ip_to_domains:
ip_to_domains[value] = set()
elif ioc_type == "INTERNET_NAME":
if value notin domain_to_ips:
domain_to_ips[value] = set()
# Identify shared hosting, registration patterns
shared_ips = {ip: domains for ip, domains in ip_to_domains.items() iflen(domains) > 1}
print(f"[+] Shared infrastructure IPs: {len(shared_ips)}")
return {"shared_ips": shared_ips, "registrar_patterns": registrar_patterns}
