ATT&CK Layer Exporter

Generate MITRE ATT&CK Navigator layer JSON files for visualization.

When to Use

Use this skill when:

•Creating executive reports on threat landscape
•Visualizing technique coverage over time
•Comparing detection capabilities
•Documenting incident scope in ATT&CK terms

Prerequisites

•Access to the DeepTempo Findings Server MCP
•Understanding of MITRE ATT&CK framework
•ATT&CK Navigator for viewing layers (optional)

ATT&CK Navigator Overview

The ATT&CK Navigator is a web-based tool for visualizing ATT&CK matrices. It uses JSON layer files to:

•Highlight specific techniques
•Show technique scores/confidence
•Add comments and metadata
•Compare multiple layers

Instructions

Step 1: Define the Scope

Determine what findings to include:

code

# Get technique rollup for time window
technique_rollup(
    time_window={
        "start": "2024-01-15T00:00:00Z",
        "end": "2024-01-15T23:59:59Z"
    },
    scope={
        "data_sources": ["flow", "dns", "waf"]
    },
    min_confidence=0.5
)

Step 2: Analyze Technique Distribution

Review the rollup results:

•Which techniques are most prevalent?
•What are the confidence levels?
•Which tactics are represented?

Step 3: Configure Layer Settings

Decide on visualization options:

Setting	Options	Recommendation
Scoring	Count, Confidence, Combined	Combined
Colors	Gradient, Discrete	Gradient
Filters	Platforms, Tactics	Based on scope
Metadata	Finding counts, Time range	Include both

Step 4: Generate the Layer

Use the export tool:

code

export_attack_layer(
    time_window={
        "start": "2024-01-15T00:00:00Z",
        "end": "2024-01-15T23:59:59Z"
    },
    scope={},
    layer_name="DeepTempo Findings - 2024-01-15"
)

Step 5: Review and Refine

Check the generated layer:

•Are technique scores appropriate?
•Are comments informative?
•Is metadata complete?

Output Format

Layer JSON Structure

json

{
    "name": "DeepTempo Findings - [Date Range]",
    "versions": {
        "attack": "14",
        "navigator": "4.9.1",
        "layer": "4.5"
    },
    "domain": "enterprise-attack",
    "description": "MITRE ATT&CK techniques detected by DeepTempo LogLM",
    "filters": {
        "platforms": ["Windows", "Linux", "macOS", "Network"]
    },
    "sorting": 3,
    "layout": {
        "layout": "side",
        "aggregateFunction": "average",
        "showID": true,
        "showName": true,
        "showAggregateScores": true,
        "countUnscored": false
    },
    "hideDisabled": false,
    "techniques": [
        {
            "techniqueID": "T1071.001",
            "tactic": "command-and-control",
            "score": 85,
            "color": "",
            "comment": "15 findings, avg confidence 0.82",
            "enabled": true,
            "metadata": [
                {"name": "finding_count", "value": "15"},
                {"name": "avg_confidence", "value": "0.82"},
                {"name": "max_confidence", "value": "0.95"},
                {"name": "data_sources", "value": "flow, dns"}
            ],
            "links": [],
            "showSubtechniques": true
        }
    ],
    "gradient": {
        "colors": ["#ffffff", "#66b3ff", "#ff6666"],
        "minValue": 0,
        "maxValue": 100
    },
    "legendItems": [
        {"label": "Low confidence", "color": "#66b3ff"},
        {"label": "High confidence", "color": "#ff6666"}
    ],
    "metadata": [
        {"name": "generated_by", "value": "DeepTempo AI SOC"},
        {"name": "generation_time", "value": "[timestamp]"},
        {"name": "time_range_start", "value": "[start]"},
        {"name": "time_range_end", "value": "[end]"},
        {"name": "total_findings", "value": "[count]"},
        {"name": "total_techniques", "value": "[count]"}
    ],
    "links": [],
    "showTacticRowBackground": true,
    "tacticRowBackground": "#dddddd",
    "selectTechniquesAcrossTactics": true,
    "selectSubtechniquesWithParent": false,
    "selectVisibleTechniques": false
}

Report Summary

Along with the JSON, provide a summary:

markdown

# ATT&CK Layer Export Summary

**Layer Name**: [Name]
**Generated**: [Timestamp]
**Time Range**: [Start] to [End]

## Coverage Summary

| Metric | Value |
|--------|-------|
| Total Findings | [n] |
| Unique Techniques | [n] |
| Tactics Covered | [n]/14 |

## Top Techniques

| Rank | Technique | Name | Findings | Avg Confidence |
|------|-----------|------|----------|----------------|
| 1 | T1071.001 | Web Protocols | 15 | 0.82 |
| 2 | T1048.003 | Exfil Over Unencrypted | 8 | 0.75 |
| ... | ... | ... | ... | ... |

## Tactic Distribution

| Tactic | Techniques | Findings |
|--------|------------|----------|
| Command and Control | 3 | 25 |
| Exfiltration | 2 | 12 |
| ... | ... | ... |

## Notable Observations

- [Observation 1]
- [Observation 2]

## Usage Instructions

1. Open [ATT&CK Navigator](https://mitre-attack.github.io/attack-navigator/)
2. Click "Open Existing Layer"
3. Select "Upload from local"
4. Choose the exported JSON file
5. Explore the visualization

---
*Layer generated by DeepTempo AI SOC*

Scoring Strategies

Strategy 1: Finding Count

Score based on number of findings per technique:

python

score = min(100, finding_count * 10)

Pros: Simple, shows volume Cons: Doesn't reflect confidence

Strategy 2: Confidence-Based

Score based on average confidence:

python

score = avg_confidence * 100

Pros: Reflects detection quality Cons: Ignores volume

Strategy 3: Combined (Recommended)

Combine count and confidence:

python

volume_factor = min(1.0, finding_count / 10)
confidence_factor = avg_confidence
score = (volume_factor * 0.4 + confidence_factor * 0.6) * 100

Pros: Balanced view Cons: More complex

Color Schemes

Confidence Gradient

json

{
    "colors": ["#ffffff", "#ff6666"],
    "minValue": 0,
    "maxValue": 100
}

White (low) → Red (high confidence)

Severity Gradient

json

{
    "colors": ["#ffffcc", "#ffeda0", "#feb24c", "#f03b20"],
    "minValue": 0,
    "maxValue": 100
}

Yellow (low) → Orange → Red (high severity)

Detection Coverage

json

{
    "colors": ["#ffffff", "#2166ac"],
    "minValue": 0,
    "maxValue": 100
}

White (no detection) → Blue (detected)

Examples

Example 1: Daily Threat Summary

Use Case: Daily report for SOC leadership Scope: Last 24 hours, all data sources Scoring: Combined (count + confidence) Output: Layer + executive summary

Example 2: Incident Documentation

Use Case: Document techniques in specific incident Scope: Incident time window, specific entities Scoring: Confidence-based Output: Layer + detailed technique analysis

Example 3: Detection Gap Analysis

Use Case: Identify techniques not being detected Scope: Last 30 days Scoring: Finding count Output: Layer highlighting gaps

Guidelines

•Match scope to purpose - Don't include irrelevant findings
•Use appropriate scoring - Choose based on audience needs
•Add meaningful comments - Help viewers understand context
•Include metadata - Document generation parameters
•Provide summary - Not everyone will open the Navigator

Constraints

•Validate technique IDs - Ensure they exist in ATT&CK
•Use current ATT&CK version - Keep version metadata accurate
•Don't overstate coverage - Low-confidence detections should show as such
•Document limitations - Note what the layer doesn't show