Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
Extract and catalog attack patterns from cyber threat intelligence reports into a structured STIX-based library mapped to MITRE ATT&CK for detection engineering and threat-informed defense.
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Cyber threat intelligence (CTI) reports from vendors like Mandiant, CrowdStrike, Talos, and Microsoft contain detailed descriptions of adversary behaviors that can be extracted, normalized, and cataloged into a structured attack pattern library. This skill covers parsing CTI reports to extract adversary techniques, mapping behaviors to MITRE ATT&CK technique IDs, creating STIX 2.1 Attack Pattern objects, building a searchable library indexed by tactic, technique, and threat actor, and generating detection rule templates from documented patterns.
stix2, mitreattack-python, spacy, requests librariesCTI reports describe adversary behaviors in natural language. Extraction involves identifying action verbs and technical terms that map to ATT&CK techniques, recognizing tool names and malware families, identifying infrastructure indicators, and mapping sequences of behaviors to attack chains (kill chain phases).
STIX defines Attack Pattern as a Structured Domain Object (SDO) that describes ways threat actors attempt to compromise targets. Each pattern links to ATT&CK via external references, includes kill chain phases (tactics), and can be related to Intrusion Sets, Malware, and Tool objects.
Extracted attack patterns inform detection engineering by providing: specific procedure examples for Sigma rule creation, behavioral sequences for correlation rules, IOC patterns for YARA and Snort rules, and data source requirements for telemetry gaps.
import re
import json
from collections import defaultdict
class CTIReportParser:
"""Parse CTI reports to extract adversary behaviors."""
BEHAVIOR_INDICATORS = [
"used", "executed", "deployed", "leveraged", "exploited",
"established", "created", "modified", "downloaded", "uploaded",
"exfiltrated", "injected", "enumerated", "spawned", "dropped",
"persisted", "escalated", "moved laterally", "collected",
"encrypted", "compressed", "encoded", "obfuscated",
]
TOOL_PATTERNS = [
r'\b(Cobalt Strike|Mimikatz|PsExec|BloodHound|Rubeus|Impacket)\b',
r'\b(PowerShell|cmd\.exe|WMI|WMIC|certutil|bitsadmin)\b',
r'\b(Metasploit|Empire|Covenant|Sliver|Brute Ratel)\b',
r'\b(Lazagne|SharpHound|ADFind|Sharphound|Invoke-Obfuscation)\b',
]
TECHNIQUE_KEYWORDS = {
"spearphishing": "T1566",
"phishing attachment": "T1566.001",
"phishing link": "T1566.002",
"powershell": "T1059.001",
"command line": "T1059.003",
"scheduled task": "T1053.005",
"registry run key": "T1547.001",
"process injection": "T1055",
"dll side-loading": "T1574.002",
"credential dumping": "T1003",
"lsass": "T1003.001",
"kerberoasting": "T1558.003",
"pass the hash": "T1550.002",
"remote desktop": "T1021.001",
"smb": "T1021.002",
"winrm": "T1021.006",
"data staging": "T1074",
"exfiltration over c2": "T1041",
"dns tunneling": "T1071.004",
"web shell": "T1505.003",
}
def parse_report(self, text, report_metadata=None):
"""Parse a CTI report and extract behaviors."""
sentences = re.split(r'[.!?]\s+', text)
behaviors = []
for sentence in sentences:
sentence_lower = sentence.lower()
# Check for behavior indicators
for indicator in self.BEHAVIOR_INDICATORS:
if indicator in sentence_lower:
behavior = {
"sentence": sentence.strip(),
"action": indicator,
"tools": self._extract_tools(sentence),
"technique_hints": self._match_techniques(sentence_lower),
}
if behavior["technique_hints"]:
behaviors.append(behavior)
break
print(f"[+] Extracted {len(behaviors)} behavioral indicators from report")
return behaviors
def _extract_tools(self, text):
"""Extract tool/malware names from text."""
tools = set()
for pattern in self.TOOL_PATTERNS:
matches = re.findall(pattern, text, re.IGNORECASE)
tools.update(matches)
return list(tools)
def _match_techniques(self, text):
"""Match text to ATT&CK technique hints."""
matches = []
for keyword, tech_id in self.TECHNIQUE_KEYWORDS.items():
if keyword in text:
matches.append({"keyword": keyword, "technique_id": tech_id})
return matches
parser = CTIReportParser()
sample_report = """
The threat actor used spearphishing attachments with macro-enabled documents to
gain initial access. Once inside, they executed PowerShell scripts to download
additional tooling. The actor leveraged Mimikatz to dump credentials from LSASS
memory. They then used pass the hash techniques for lateral movement via SMB
to multiple systems. Data was staged in a compressed archive and exfiltrated
over the existing C2 channel. The actor established persistence through
scheduled tasks and registry run keys.
"""
behaviors = parser.parse_report(sample_report)
from attackcti import attack_client
class ATTACKMapper:
def __init__(self):
self.lift = attack_client()
self.techniques = {}
self._load_techniques()
def _load_techniques(self):
"""Load all ATT&CK techniques for mapping."""
all_techs = self.lift.get_enterprise_techniques()
for tech in all_techs:
tech_id = ""
for ref in tech.get("external_references", []):
if ref.get("source_name") == "mitre-attack":
tech_id = ref.get("external_id", "")
break
if tech_id:
self.techniques[tech_id] = {
"name": tech.get("name", ""),
"description": tech.get("description", "")[:500],
"tactics": [p.get("phase_name") for p in tech.get("kill_chain_phases", [])],
"platforms": tech.get("x_mitre_platforms", []),
"data_sources": tech.get("x_mitre_data_sources", []),
}
print(f"[+] Loaded {len(self.techniques)} ATT&CK techniques")
def map_behaviors(self, behaviors):
"""Map extracted behaviors to ATT&CK techniques."""
mapped = []
for behavior in behaviors:
for hint in behavior.get("technique_hints", []):
tech_id = hint["technique_id"]
if tech_id in self.techniques:
tech_info = self.techniques[tech_id]
mapped.append({
"technique_id": tech_id,
"technique_name": tech_info["name"],
"tactics": tech_info["tactics"],
"source_sentence": behavior["sentence"],
"tools_observed": behavior["tools"],
"keyword_matched": hint["keyword"],
"data_sources": tech_info["data_sources"],
})
print(f"[+] Mapped {len(mapped)} behaviors to ATT&CK techniques")
return mapped
mapper = ATTACKMapper()
mapped_behaviors = mapper.map_behaviors(behaviors)
from stix2 import AttackPattern, Relationship, Bundle, TLP_GREEN
from datetime import datetime
class AttackPatternLibrary:
def __init__(self):
self.patterns = []
self.relationships = []
def add_pattern_from_mapping(self, mapping, report_source="CTI Report"):
"""Create STIX Attack Pattern from mapped behavior."""
pattern = AttackPattern(
name=mapping["technique_name"],
description=f"Observed: {mapping['source_sentence']}\n\n"
f"Tools: {', '.join(mapping['tools_observed']) or 'None identified'}\n"
f"Source: {report_source}",
external_references=[{
"source_name": "mitre-attack",
"external_id": mapping["technique_id"],
"url": f"https://attack.mitre.org/techniques/{mapping['technique_id'].replace('.', '/')}/",
}],
kill_chain_phases=[{
"kill_chain_name": "mitre-attack",
"phase_name": tactic,
} for tactic in mapping["tactics"]],
object_marking_refs=[TLP_GREEN],
)
self.patterns.append(pattern)
return pattern
def build_library(self, mapped_behaviors, report_source="CTI Report"):
"""Build complete attack pattern library from mappings."""
seen_techniques = set()
for mapping in mapped_behaviors:
tech_id = mapping["technique_id"]
if tech_id not in seen_techniques:
self.add_pattern_from_mapping(mapping, report_source)
seen_techniques.add(tech_id)
bundle = Bundle(objects=self.patterns + self.relationships)
print(f"[+] Library: {len(self.patterns)} attack patterns")
return bundle
def export_library(self, output_file="attack_pattern_library.json"):
bundle = Bundle(objects=self.patterns + self.relationships)
with open(output_file, "w") as f:
f.write(bundle.serialize(pretty=True))
print(f"[+] Library exported to {output_file}")
def generate_detection_templates(self, mapped_behaviors):
"""Generate Sigma rule templates from attack patterns."""
templates = []
for mapping in mapped_behaviors:
template = {
"title": f"Detection: {mapping['technique_name']} ({mapping['technique_id']})",
"status": "experimental",
"description": f"Detects {mapping['technique_name']} based on CTI report observation",
"references": [
f"https://attack.mitre.org/techniques/{mapping['technique_id'].replace('.', '/')}/",
],
"tags": [
f"attack.{mapping['tactics'][0]}" if mapping['tactics'] else "attack.unknown",
f"attack.{mapping['technique_id'].lower()}",
],
"data_sources": mapping.get("data_sources", []),
"observed_tools": mapping.get("tools_observed", []),
"source_context": mapping["source_sentence"],
}
templates.append(template)
with open("detection_templates.json", "w") as f:
json.dump(templates, f, indent=2)
print(f"[+] Generated {len(templates)} detection templates")
return templates
library = AttackPatternLibrary()
bundle = library.build_library(mapped_behaviors, "Sample CTI Report")
library.export_library()
templates = library.generate_detection_templates(mapped_behaviors)
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。