mitre-attack-framework（斜接攻击框架）

MITRE ATT&CK Framework⁠‍⁠​‌​‌​​‌‌‍​‌​​‌​‌‌‍​​‌‌​​​‌‍​‌​​‌‌​​‍​​​​​​​‌‍‌​​‌‌​‌​‍‌​​​​​​​‍‌‌​​‌‌‌‌‍‌‌​​​‌​​‍‌‌‌‌‌‌​‌‍‌‌​‌​​​​‍​‌​‌‌‌‌‌‍​‌​​‌​‌‌‍​‌‌​‌​​‌‍‌​‌​‌‌‌​‍​​‌​‌​​​‍‌‌‌​‌​‌‌‍​‌‌‌​‌​‌‍​​​​‌‌‌​‍‌‌‌‌‌‌​​‍‌​‌​‌​​‌‍​​​​‌​‌​‍‌​​‌​​‌​⁠‍⁠

Overview

MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) is a globally-accessible knowledge base of adversary behavior based on real-world observations. Created by MITRE Corporation, it has become the universal language for describing how adversaries operate.

References

• Website: https://attack.mitre.org/
• Navigator: https://mitre-attack.github.io/attack-navigator/
• GitHub: https://github.com/mitre-attack

Core Philosophy

"Know your adversary."

"You can't defend against what you don't understand."

ATT&CK shifts the focus from IOCs (what attackers use) to TTPs (how attackers behave). This behavioral focus provides more durable detection strategies.

The ATT&CK Matrix Structure

Enterprise ATT&CK Matrix

TACTICS (The "Why" - Adversary Goals)
├── Reconnaissance        ← Gather information
├── Resource Development  ← Build infrastructure
├── Initial Access        ← Get into the network
├── Execution             ← Run malicious code
├── Persistence           ← Maintain foothold
├── Privilege Escalation  ← Get higher privileges
├── Defense Evasion       ← Avoid detection
├── Credential Access     ← Steal credentials
├── Discovery             ← Learn the environment
├── Lateral Movement      ← Move through network
├── Collection            ← Gather target data
├── Command and Control   ← Communicate with implants
├── Exfiltration          ← Steal data
└── Impact                ← Damage or disrupt

TECHNIQUES (The "How" - Methods Used)
└── Each tactic contains multiple techniques
    └── Techniques may have sub-techniques
        └── Example: T1059.001 (PowerShell) under T1059 (Command and Scripting Interpreter)

Key Components

When Implementing

Always

• Map detections to ATT&CK techniques
• Assess coverage across all tactics
• Use ATT&CK Navigator for visualization
• Reference technique IDs in alerts
• Track adversary groups and their TTPs
• Update mapping as ATT&CK evolves

Never

• Assume full coverage from a few detections
• Ignore techniques without current detections
• Treat ATT&CK as a compliance checklist
• Map inaccurately to inflate coverage
• Forget sub-techniques in analysis

Prefer

• Behavioral detection over signature matching
• Coverage breadth over depth initially
• Technique-based hunting hypotheses
• ATT&CK-aligned threat intelligence
• Continuous coverage assessment

Implementation Patterns

ATT&CK Coverage Assessment

# attack_coverage.py
# Assess and visualize ATT&CK coverage

from dataclasses import dataclass, field
from typing import List, Dict, Optional, Set
from enum import Enum
import json

class CoverageLevel(Enum):
    NONE = 0
    MINIMAL = 1      # Basic detection exists
    PARTIAL = 2      # Some variants covered
    SUBSTANTIAL = 3  # Most variants covered
    COMPREHENSIVE = 4 # Full coverage with validation

@dataclass
class Technique:
    """ATT&CK Technique representation"""
    id: str                          # e.g., "T1003.001"
    name: str                        # e.g., "LSASS Memory"
    tactic: str                      # e.g., "Credential Access"
    platforms: List[str]             # e.g., ["Windows"]
    data_sources: List[str]          # Required telemetry

    # Coverage tracking
    detections: List[str] = field(default_factory=list)
    coverage_level: CoverageLevel = CoverageLevel.NONE
    last_validated: Optional[str] = None

    @property
    def is_sub_technique(self) -> bool:
        return "." in self.id

@dataclass
class Detection:
    """A detection rule mapped to ATT&CK"""
    name: str
    techniques: List[str]           # ATT&CK technique IDs
    query: str
    platform: str                   # SIEM/EDR platform
    false_positive_rate: float
    validated: bool = False

class ATTACKCoverageAnalyzer:
    """Analyze detection coverage against ATT&CK"""

    def __init__(self):
        self.techniques: Dict[str, Technique] = {}
        self.detections: List[Detection] = []
        self.tactics = [
            "Reconnaissance", "Resource Development", "Initial Access",
            "Execution", "Persistence", "Privilege Escalation",
            "Defense Evasion", "Credential Access", "Discovery",
            "Lateral Movement", "Collection", "Command and Control",
            "Exfiltration", "Impact"
        ]

    def load_attack_data(self, attack_json_path: str):
        """Load ATT&CK STIX data"""
        # In practice, load from MITRE's STIX bundle
        pass

    def add_detection(self, detection: Detection):
        """Add detection and update coverage"""
        self.detections.append(detection)

        for tech_id in detection.techniques:
            if tech_id in self.techniques:
                self.techniques[tech_id].detections.append(detection.name)
                self._update_coverage_level(tech_id)

    def _update_coverage_level(self, tech_id: str):
        """Recalculate coverage level for technique"""
        tech = self.techniques[tech_id]
        detection_count = len(tech.detections)

        if detection_count == 0:
            tech.coverage_level = CoverageLevel.NONE
        elif detection_count == 1:
            tech.coverage_level = CoverageLevel.MINIMAL
        elif detection_count < 3:
            tech.coverage_level = CoverageLevel.PARTIAL
        elif detection_count < 5:
            tech.coverage_level = CoverageLevel.SUBSTANTIAL
        else:
            tech.coverage_level = CoverageLevel.COMPREHENSIVE

    def coverage_by_tactic(self) -> Dict[str, dict]:
        """Get coverage statistics per tactic"""
        results = {}

        for tactic in self.tactics:
            tactic_techs = [t for t in self.techniques.values()
                          if t.tactic == tactic]

            if not tactic_techs:
                continue

            covered = sum(1 for t in tactic_techs
                         if t.coverage_level != CoverageLevel.NONE)

            results[tactic] = {
                'total_techniques': len(tactic_techs),
                'covered': covered,
                'coverage_percent': (covered / len(tactic_techs)) * 100,
                'gaps': [t.id for t in tactic_techs
                        if t.coverage_level == CoverageLevel.NONE]
            }

        return results

    def identify_gaps(self) -> List[Technique]:
        """Find techniques with no detection coverage"""
        return [t for t in self.techniques.values()
                if t.coverage_level == CoverageLevel.NONE]

    def prioritize_gaps(self) -> List[Technique]:
        """Prioritize gaps by adversary usage"""
        gaps = self.identify_gaps()

        # Prioritize by:
        # 1. Commonly used by threat groups
        # 2. Data sources already available
        # 3. Tactic importance

        high_priority_tactics = [
            "Initial Access", "Execution", "Persistence",
            "Credential Access", "Lateral Movement"
        ]

        prioritized = sorted(
            gaps,
            key=lambda t: (
                t.tactic in high_priority_tactics,  # Prioritize key tactics
                len(t.data_sources)                  # Easier to detect
            ),
            reverse=True
        )

        return prioritized

    def generate_navigator_layer(self) -> dict:
        """Generate ATT&CK Navigator layer JSON"""
        layer = {
            "name": "Detection Coverage",
            "version": "4.4",
            "domain": "enterprise-attack",
            "description": "Current detection coverage",
            "techniques": []
        }

        color_map = {
            CoverageLevel.NONE: "#ffffff",
            CoverageLevel.MINIMAL: "#ffcccc",
            CoverageLevel.PARTIAL: "#ffff99",
            CoverageLevel.SUBSTANTIAL: "#99ff99",
            CoverageLevel.COMPREHENSIVE: "#00ff00"
        }

        for tech in self.techniques.values():
            layer["techniques"].append({
                "techniqueID": tech.id,
                "color": color_map[tech.coverage_level],
                "comment": f"Detections: {len(tech.detections)}"
            })

        return layer

Technique-Based Detection

# technique_detection.py
# Build detections aligned to ATT&CK techniques

from dataclasses import dataclass
from typing import List, Dict

@dataclass
class ATTACKDetection:
    """Detection rule with full ATT&CK context"""

    # ATT&CK mapping
    technique_id: str
    technique_name: str
    tactic: str

    # Detection details
    name: str
    description: str
    query: str
    platform: str

    # Quality metrics
    severity: str
    confidence: str
    false_positive_guidance: str

    # Data requirements
    data_sources: List[str]

    # Response
    recommended_response: List[str]

    def to_sigma(self) -> str:
        """Export as Sigma rule"""
        return f"""
title: {self.name}
id: {self.technique_id.lower().replace('.', '-')}-detection
status: experimental
description: {self.description}
references:
    - https://attack.mitre.org/techniques/{self.technique_id}/
author: Security Team
date: 2024/01/01
tags:
    - attack.{self.tactic.lower().replace(' ', '_')}
    - attack.{self.technique_id.lower()}
logsource:
    category: process_creation
    product: windows
detection:
    selection:
        # Detection logic here
    condition: selection
falsepositives:
    - {self.false_positive_guidance}
level: {self.severity.lower()}
"""

# Example: T1003.001 - LSASS Memory
lsass_detection = ATTACKDetection(
    technique_id="T1003.001",
    technique_name="LSASS Memory",
    tactic="Credential Access",
    name="LSASS Memory Access via Suspicious Process",
    description="Detects processes accessing LSASS memory, "
                "commonly used for credential dumping",
    query="""
        event.code:10 AND
        winlog.event_data.TargetImage:*lsass.exe AND
        NOT winlog.event_data.SourceImage:(
            *MsMpEng.exe OR *csrss.exe OR *wininit.exe
        )
    """,
    platform="Elastic SIEM",
    severity="High",
    confidence="Medium",
    false_positive_guidance="Security tools may legitimately access LSASS",
    data_sources=["Process: Process Access (Sysmon EID 10)"],
    recommended_response=[
        "Isolate affected host",
        "Capture memory dump for analysis",
        "Reset potentially compromised credentials",
        "Hunt for lateral movement"
    ]
)

Threat Group Tracking

# threat_groups.py
# Track adversary groups and their TTPs

from dataclasses import dataclass
from typing import List, Set, Dict

@dataclass
class ThreatGroup:
    """Known adversary group with TTPs"""

    id: str                          # e.g., "G0007"
    name: str                        # e.g., "APT28"
    aliases: List[str]               # e.g., ["Fancy Bear", "Sofacy"]

    # Attribution
    suspected_origin: str
    target_sectors: List[str]
    target_regions: List[str]

    # TTPs
    techniques: List[str]            # ATT&CK technique IDs

    # Intelligence
    first_seen: str
    last_seen: str
    active: bool

    def technique_overlap(self, other: 'ThreatGroup') -> Set[str]:
        """Find common techniques with another group"""
        return set(self.techniques) & set(other.techniques)

    def unique_techniques(self, all_groups: List['ThreatGroup']) -> Set[str]:
        """Find techniques unique to this group"""
        other_techniques = set()
        for group in all_groups:
            if group.id != self.id:
                other_techniques.update(group.techniques)

        return set(self.techniques) - other_techniques

class ThreatIntelligence:
    """Manage threat group intelligence"""

    def __init__(self):
        self.groups: Dict[str, ThreatGroup] = {}

    def relevant_groups(self, sector: str, region: str) -> List[ThreatGroup]:
        """Find groups targeting specific sector/region"""
        return [
            g for g in self.groups.values()
            if (sector in g.target_sectors or "All" in g.target_sectors) and
               (region in g.target_regions or "Global" in g.target_regions) and
               g.active
        ]

    def priority_techniques(self, sector: str, region: str) -> Dict[str, int]:
        """Rank techniques by threat relevance"""
        relevant = self.relevant_groups(sector, region)

        technique_counts = {}
        for group in relevant:
            for tech in group.techniques:
                technique_counts[tech] = technique_counts.get(tech, 0) + 1

        # Sort by frequency across relevant groups
        return dict(sorted(
            technique_counts.items(),
            key=lambda x: x[1],
            reverse=True
        ))

    def detection_priorities(self,
                            sector: str,
                            region: str,
                            current_coverage: Set[str]) -> List[str]:
        """Prioritize detections based on threat landscape"""
        priority_techs = self.priority_techniques(sector, region)

        # Find high-priority techniques without coverage
        gaps = [
            tech for tech, count in priority_techs.items()
            if tech not in current_coverage and count >= 2
        ]

        return gaps[:10]  # Top 10 priorities

Hunt Hypothesis Generation

# hypothesis_generator.py
# Generate hunt hypotheses from ATT&CK

from dataclasses import dataclass
from typing import List

@dataclass
class HuntHypothesis:
    """ATT&CK-based hunt hypothesis"""

    technique_id: str
    technique_name: str
    tactic: str

    hypothesis: str
    rationale: str

    data_requirements: List[str]
    hunt_query: str

    expected_findings: List[str]
    success_criteria: str

def generate_hypotheses(technique_id: str,
                       technique_data: dict) -> List[HuntHypothesis]:
    """Generate hunt hypotheses for a technique"""

    hypotheses = []

    # Hypothesis 1: Direct technique detection
    hypotheses.append(HuntHypothesis(
        technique_id=technique_id,
        technique_name=technique_data['name'],
        tactic=technique_data['tactic'],
        hypothesis=f"Adversaries are using {technique_data['name']} "
                   f"in our environment",
        rationale=f"This technique is commonly used by threat groups "
                  f"targeting our sector",
        data_requirements=technique_data['data_sources'],
        hunt_query=technique_data.get('detection_query', ''),
        expected_findings=[
            "Legitimate use by IT/security tools",
            "Developer/admin testing",
            "Potential adversary activity"
        ],
        success_criteria="Identify all instances, triage findings, "
                        "create detection for confirmed malicious patterns"
    ))

    # Hypothesis 2: Technique chaining
    if technique_data.get('related_techniques'):
        hypotheses.append(HuntHypothesis(
            technique_id=technique_id,
            technique_name=technique_data['name'],
            tactic=technique_data['tactic'],
            hypothesis=f"Adversaries using {technique_data['name']} "
                       f"are also using related techniques",
            rationale="Techniques are often used in combination",
            data_requirements=technique_data['data_sources'],
            hunt_query="Correlated query across related techniques",
            expected_findings=["Attack chains", "Lateral movement paths"],
            success_criteria="Map complete attack chain if present"
        ))

    return hypotheses

Mental Model

MITRE ATT&CK practitioners ask:

1. What tactic is this? Understand adversary goal
2. What technique? Identify specific method
3. Do we detect this? Assess coverage
4. Who uses this? Threat group attribution
5. What's the gap? Prioritize improvements

Signature ATT&CK Moves

• Technique IDs in all detections and alerts
• Navigator layers for coverage visualization
• Threat group TTP tracking
• Gap analysis by tactic
• Hypothesis generation from techniques
• Continuous coverage assessment