feat(phase-27): add advanced ATT&CK Navigator-style heatmap with layers, filters and export (T-221 to T-223)
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526
backend/app/routers/heatmap.py
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526
backend/app/routers/heatmap.py
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"""Heatmap endpoints — ATT&CK Navigator-compatible layer generation.
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Provides multiple layer types (coverage, threat actor, detection rules,
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campaign) and an export endpoint that produces a JSON file importable
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by the official MITRE ATT&CK Navigator.
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"""
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from typing import Optional, List
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from fastapi import APIRouter, Depends, HTTPException, Query
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from fastapi.responses import StreamingResponse
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from sqlalchemy import func
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from sqlalchemy.orm import Session
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import io
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import json
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from app.database import get_db
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from app.dependencies.auth import get_current_user
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from app.models.user import User
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from app.models.technique import Technique
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from app.models.test import Test
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from app.models.threat_actor import ThreatActor, ThreatActorTechnique
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from app.models.detection_rule import DetectionRule
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from app.models.campaign import Campaign, CampaignTest
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from app.models.defensive_technique import DefensiveTechniqueMapping
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from app.models.enums import TechniqueStatus, TestState
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router = APIRouter(prefix="/heatmap", tags=["heatmap"])
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# ── Constants ─────────────────────────────────────────────────────────
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ATTACK_VERSION = "15"
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NAVIGATOR_VERSION = "5.0"
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LAYER_VERSION = "4.5"
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DOMAIN = "enterprise-attack"
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# Score mapping for technique status_global
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STATUS_SCORE_MAP = {
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TechniqueStatus.validated: 100,
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TechniqueStatus.partial: 60,
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TechniqueStatus.in_progress: 30,
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TechniqueStatus.not_covered: 10,
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TechniqueStatus.not_evaluated: 0,
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TechniqueStatus.review_required: 10,
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}
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# ── Helpers ───────────────────────────────────────────────────────────
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def _score_to_color(score: int) -> str:
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"""Map a 0-100 score to a red → yellow → green color hex."""
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if score <= 0:
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return "#d3d3d3" # gray for not evaluated
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if score <= 25:
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return "#ff6666" # red
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if score <= 50:
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return "#ff9933" # orange
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if score <= 75:
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return "#ffff66" # yellow
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return "#66ff66" # green
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def _build_layer_skeleton(
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name: str,
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description: str,
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gradient_colors: List[str] | None = None,
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) -> dict:
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"""Return a base layer dict compatible with ATT&CK Navigator."""
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return {
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"name": name,
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"versions": {
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"attack": ATTACK_VERSION,
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"navigator": NAVIGATOR_VERSION,
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"layer": LAYER_VERSION,
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},
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"domain": DOMAIN,
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"description": description,
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"filters": {"platforms": ["windows", "linux", "macos"]},
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"gradient": {
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"colors": gradient_colors or ["#ff6666", "#ffff66", "#66ff66"],
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"minValue": 0,
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"maxValue": 100,
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},
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"techniques": [],
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}
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def _apply_filters(
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query,
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model,
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platforms: Optional[List[str]] = None,
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tactics: Optional[List[str]] = None,
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):
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"""Apply common platform and tactic filters to a technique query."""
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if platforms:
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from sqlalchemy import or_, cast, String
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from sqlalchemy.dialects.postgresql import JSONB
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# Filter techniques that have any of the specified platforms
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platform_filters = []
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for platform in platforms:
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platform_filters.append(
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model.platforms.op("@>")(json.dumps([platform]))
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)
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if platform_filters:
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query = query.filter(or_(*platform_filters))
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if tactics:
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from sqlalchemy import or_
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tactic_filters = []
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for tactic in tactics:
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tactic_filters.append(model.tactic.ilike(f"%{tactic}%"))
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query = query.filter(or_(*tactic_filters))
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return query
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def _format_tactic(tactic_str: str | None) -> str:
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"""Normalize tactic string to ATT&CK Navigator format (kebab-case)."""
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if not tactic_str:
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return ""
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# Take first tactic if comma-separated
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first = tactic_str.split(",")[0].strip().lower()
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return first
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def _get_technique_metadata(technique, db: Session) -> list:
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"""Build metadata array for a technique."""
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# Count validated tests
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test_count = (
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db.query(func.count(Test.id))
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.filter(Test.technique_id == technique.id, Test.state == TestState.validated)
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.scalar()
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) or 0
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# Count detection rules
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rule_count = (
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db.query(func.count(DetectionRule.id))
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.filter(DetectionRule.mitre_technique_id == technique.mitre_id)
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.scalar()
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) or 0
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metadata = [
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{"name": "tests_count", "value": str(test_count)},
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{"name": "detection_rules", "value": str(rule_count)},
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]
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if technique.last_review_date:
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metadata.append(
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{"name": "last_validated", "value": technique.last_review_date.strftime("%Y-%m-%d")}
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)
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return metadata
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# ── GET /heatmap/coverage ─────────────────────────────────────────────
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@router.get("/coverage")
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def heatmap_coverage(
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platforms: Optional[str] = Query(None, description="Comma-separated platforms"),
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tactics: Optional[str] = Query(None, description="Comma-separated tactics"),
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min_score: int = Query(0, ge=0, le=100),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""Coverage layer — score based on status_global of each technique."""
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layer = _build_layer_skeleton("Aegis Coverage", "Coverage layer generated by Aegis")
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query = db.query(Technique)
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platform_list = [p.strip() for p in platforms.split(",")] if platforms else None
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tactic_list = [t.strip() for t in tactics.split(",")] if tactics else None
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query = _apply_filters(query, Technique, platform_list, tactic_list)
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techniques = query.all()
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for tech in techniques:
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score = STATUS_SCORE_MAP.get(tech.status_global, 0)
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if score < min_score:
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continue
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comment_parts = [f"Status: {tech.status_global.value}"]
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metadata = _get_technique_metadata(tech, db)
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# Enrich comment with test/rule info
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tests_info = next((m for m in metadata if m["name"] == "tests_count"), None)
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rules_info = next((m for m in metadata if m["name"] == "detection_rules"), None)
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if tests_info:
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comment_parts.append(f"{tests_info['value']} tests validated")
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if rules_info:
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comment_parts.append(f"{rules_info['value']} detection rules")
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layer["techniques"].append({
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"techniqueID": tech.mitre_id,
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"tactic": _format_tactic(tech.tactic),
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"color": _score_to_color(score),
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"score": score,
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"comment": " - ".join(comment_parts),
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"enabled": True,
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"metadata": metadata,
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})
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return layer
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# ── GET /heatmap/threat-actor/{actor_id} ──────────────────────────────
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@router.get("/threat-actor/{actor_id}")
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def heatmap_threat_actor(
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actor_id: str,
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platforms: Optional[str] = Query(None),
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tactics: Optional[str] = Query(None),
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min_score: int = Query(0, ge=0, le=100),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""Threat actor layer — techniques used by an actor with coverage color."""
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actor = db.query(ThreatActor).filter(ThreatActor.id == actor_id).first()
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if not actor:
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raise HTTPException(status_code=404, detail="Threat actor not found")
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layer = _build_layer_skeleton(
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f"Threat Actor: {actor.name}",
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f"Techniques used by {actor.name} with coverage overlay",
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gradient_colors=["#808080", "#ff6666", "#66ff66"],
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)
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# Get actor's technique IDs
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actor_technique_rows = (
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db.query(ThreatActorTechnique)
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.filter(ThreatActorTechnique.threat_actor_id == actor.id)
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.all()
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)
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actor_technique_ids = {row.technique_id for row in actor_technique_rows}
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if not actor_technique_ids:
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return layer
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query = db.query(Technique)
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platform_list = [p.strip() for p in platforms.split(",")] if platforms else None
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tactic_list = [t.strip() for t in tactics.split(",")] if tactics else None
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query = _apply_filters(query, Technique, platform_list, tactic_list)
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techniques = query.all()
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for tech in techniques:
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is_actor_technique = tech.id in actor_technique_ids
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score = STATUS_SCORE_MAP.get(tech.status_global, 0) if is_actor_technique else 0
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if is_actor_technique and score < min_score:
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continue
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if is_actor_technique:
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metadata = _get_technique_metadata(tech, db)
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layer["techniques"].append({
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"techniqueID": tech.mitre_id,
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"tactic": _format_tactic(tech.tactic),
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"color": _score_to_color(score),
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"score": score,
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"comment": f"Used by {actor.name} - Coverage: {tech.status_global.value}",
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"enabled": True,
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"metadata": metadata,
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})
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else:
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layer["techniques"].append({
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"techniqueID": tech.mitre_id,
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"tactic": _format_tactic(tech.tactic),
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"color": "",
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"score": 0,
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"comment": "",
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"enabled": False,
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"metadata": [],
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})
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return layer
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# ── GET /heatmap/detection-rules ──────────────────────────────────────
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@router.get("/detection-rules")
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def heatmap_detection_rules(
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platforms: Optional[str] = Query(None),
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tactics: Optional[str] = Query(None),
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min_score: int = Query(0, ge=0, le=100),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""Detection rules layer — score based on ratio of rules available vs total."""
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layer = _build_layer_skeleton(
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"Detection Rules Coverage",
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"Coverage of detection rules per technique",
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)
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query = db.query(Technique)
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platform_list = [p.strip() for p in platforms.split(",")] if platforms else None
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tactic_list = [t.strip() for t in tactics.split(",")] if tactics else None
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query = _apply_filters(query, Technique, platform_list, tactic_list)
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techniques = query.all()
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# Get rule counts per technique_mitre_id in one query
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rule_counts = dict(
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db.query(
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DetectionRule.mitre_technique_id,
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func.count(DetectionRule.id),
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)
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.filter(DetectionRule.is_active == True)
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.group_by(DetectionRule.mitre_technique_id)
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.all()
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)
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# Find the max rule count for normalization
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max_rules = max(rule_counts.values()) if rule_counts else 1
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from app.models.test_detection_result import TestDetectionResult
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# Get evaluated rule counts per technique
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evaluated_counts_raw = (
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db.query(
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DetectionRule.mitre_technique_id,
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func.count(TestDetectionResult.id),
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)
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.join(TestDetectionResult, TestDetectionResult.detection_rule_id == DetectionRule.id)
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.filter(TestDetectionResult.triggered.isnot(None))
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.group_by(DetectionRule.mitre_technique_id)
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.all()
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)
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evaluated_counts = dict(evaluated_counts_raw)
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for tech in techniques:
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total_rules = rule_counts.get(tech.mitre_id, 0)
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evaluated_rules = evaluated_counts.get(tech.mitre_id, 0)
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if total_rules > 0:
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# Score based on rule availability (normalized) and evaluation ratio
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availability_score = min((total_rules / max_rules) * 50, 50)
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evaluation_score = (evaluated_rules / total_rules) * 50 if total_rules > 0 else 0
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score = int(min(availability_score + evaluation_score, 100))
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else:
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score = 0
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if score < min_score:
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continue
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layer["techniques"].append({
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"techniqueID": tech.mitre_id,
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"tactic": _format_tactic(tech.tactic),
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"color": _score_to_color(score),
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"score": score,
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"comment": f"{total_rules} rules available, {evaluated_rules} evaluated",
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"enabled": True,
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"metadata": [
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{"name": "total_rules", "value": str(total_rules)},
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{"name": "evaluated_rules", "value": str(evaluated_rules)},
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],
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})
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return layer
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# ── GET /heatmap/campaign/{campaign_id} ───────────────────────────────
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@router.get("/campaign/{campaign_id}")
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def heatmap_campaign(
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campaign_id: str,
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platforms: Optional[str] = Query(None),
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tactics: Optional[str] = Query(None),
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min_score: int = Query(0, ge=0, le=100),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""Campaign layer — only techniques in the campaign, colored by test state."""
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campaign = db.query(Campaign).filter(Campaign.id == campaign_id).first()
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if not campaign:
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raise HTTPException(status_code=404, detail="Campaign not found")
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layer = _build_layer_skeleton(
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f"Campaign: {campaign.name}",
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f"Progress of campaign '{campaign.name}'",
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)
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# Get campaign tests with their associated techniques
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campaign_tests = (
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db.query(CampaignTest)
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.filter(CampaignTest.campaign_id == campaign.id)
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.all()
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)
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if not campaign_tests:
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return layer
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# Map test_id -> test for all tests in campaign
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test_ids = [ct.test_id for ct in campaign_tests]
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tests = db.query(Test).filter(Test.id.in_(test_ids)).all()
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test_map = {t.id: t for t in tests}
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# Map technique_id -> technique
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technique_ids = {t.technique_id for t in tests if t.technique_id}
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techniques = db.query(Technique).filter(Technique.id.in_(technique_ids)).all()
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tech_map = {t.id: t for t in techniques}
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# Score mapping for test states
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test_state_score = {
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TestState.validated: 100,
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TestState.in_review: 70,
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TestState.blue_evaluating: 50,
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TestState.red_executing: 30,
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TestState.draft: 10,
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TestState.rejected: 5,
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}
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# Group by technique (a technique may have multiple tests in a campaign)
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tech_scores: dict = {}
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for ct in campaign_tests:
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test = test_map.get(ct.test_id)
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if not test:
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continue
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tech = tech_map.get(test.technique_id)
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if not tech:
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continue
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state_score = test_state_score.get(test.state, 0)
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if tech.mitre_id not in tech_scores:
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tech_scores[tech.mitre_id] = {
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"technique": tech,
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"max_score": state_score,
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"tests": [],
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}
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else:
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tech_scores[tech.mitre_id]["max_score"] = max(
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tech_scores[tech.mitre_id]["max_score"], state_score
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)
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tech_scores[tech.mitre_id]["tests"].append(test)
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platform_list = [p.strip() for p in platforms.split(",")] if platforms else None
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tactic_list = [t.strip() for t in tactics.split(",")] if tactics else None
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for mitre_id, info in tech_scores.items():
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tech = info["technique"]
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score = info["max_score"]
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# Apply filters
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if platform_list:
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tech_platforms = tech.platforms or []
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if not any(p in tech_platforms for p in platform_list):
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continue
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if tactic_list:
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tech_tactics = (tech.tactic or "").lower().split(",")
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tech_tactics = [t.strip() for t in tech_tactics]
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if not any(t in tech_tactics for t in tactic_list):
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continue
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if score < min_score:
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continue
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test_states = [t.state.value for t in info["tests"]]
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layer["techniques"].append({
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"techniqueID": mitre_id,
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"tactic": _format_tactic(tech.tactic),
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"color": _score_to_color(score),
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"score": score,
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"comment": f"Campaign tests: {', '.join(test_states)}",
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"enabled": True,
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"metadata": [
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{"name": "campaign_tests", "value": str(len(info["tests"]))},
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{"name": "best_state", "value": max(test_states) if test_states else "none"},
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],
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})
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return layer
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# ── GET /heatmap/export-navigator ─────────────────────────────────────
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|
||||
@router.get("/export-navigator")
|
||||
def export_navigator(
|
||||
layer: str = Query(..., description="Layer type: coverage, threat-actor, detection-rules, campaign"),
|
||||
layer_id: Optional[str] = Query(None, description="Actor ID or Campaign ID (if applicable)"),
|
||||
platforms: Optional[str] = Query(None),
|
||||
tactics: Optional[str] = Query(None),
|
||||
min_score: int = Query(0, ge=0, le=100),
|
||||
db: Session = Depends(get_db),
|
||||
current_user: User = Depends(get_current_user),
|
||||
):
|
||||
"""Export a heatmap layer as a downloadable JSON file for ATT&CK Navigator."""
|
||||
# Delegate to the appropriate layer endpoint
|
||||
if layer == "coverage":
|
||||
data = heatmap_coverage(
|
||||
platforms=platforms, tactics=tactics, min_score=min_score,
|
||||
db=db, current_user=current_user,
|
||||
)
|
||||
elif layer == "threat-actor":
|
||||
if not layer_id:
|
||||
raise HTTPException(status_code=400, detail="layer_id required for threat-actor layer")
|
||||
data = heatmap_threat_actor(
|
||||
actor_id=layer_id, platforms=platforms, tactics=tactics,
|
||||
min_score=min_score, db=db, current_user=current_user,
|
||||
)
|
||||
elif layer == "detection-rules":
|
||||
data = heatmap_detection_rules(
|
||||
platforms=platforms, tactics=tactics, min_score=min_score,
|
||||
db=db, current_user=current_user,
|
||||
)
|
||||
elif layer == "campaign":
|
||||
if not layer_id:
|
||||
raise HTTPException(status_code=400, detail="layer_id required for campaign layer")
|
||||
data = heatmap_campaign(
|
||||
campaign_id=layer_id, platforms=platforms, tactics=tactics,
|
||||
min_score=min_score, db=db, current_user=current_user,
|
||||
)
|
||||
else:
|
||||
raise HTTPException(status_code=400, detail=f"Unknown layer type: {layer}")
|
||||
|
||||
# Convert to JSON and return as downloadable file
|
||||
json_content = json.dumps(data, indent=2, default=str)
|
||||
buffer = io.BytesIO(json_content.encode("utf-8"))
|
||||
filename = f"aegis_{layer}_layer.json"
|
||||
|
||||
return StreamingResponse(
|
||||
buffer,
|
||||
media_type="application/json",
|
||||
headers={
|
||||
"Content-Disposition": f"attachment; filename={filename}",
|
||||
},
|
||||
)
|
||||
Reference in New Issue
Block a user