# Ember Malware Detection Eval

> Evaluates the performance and robustness of various machine learning classifiers for static malware detection on high-dimensional tabular features, specifically assessing the impact of dimensionality reduction techniques (PCA, LDA) on classification accuracy and discriminative power. Use when the user wants to benchmark on EMBER, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/ember-malware-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ember-malware-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ember-malware-detection-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ember-malware-detection-eval

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# ember-malware-detection-eval

> Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset — Abedin et al. (2025) (arXiv:2507.16952, 2025)

## What this evaluates

Evaluates the performance and robustness of various machine learning classifiers for static malware detection on high-dimensional tabular features, specifically assessing the impact of dimensionality reduction techniques (PCA, LDA) on classification accuracy and discriminative power.

## Datasets

- **EMBER** — total ?; splits: test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly classified instances out of the total number of instances.
- `precision` — range: [0, 1]
  - Ratio of true positive predictions to the total number of positive predictions.
- `recall` — range: [0, 1]
  - Ratio of true positive predictions to the total number of actual positive instances.
- `F1-score` — range: [0, 1]
  - Harmonic mean of precision and recall.
- `AUC-ROC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve, measuring the model's ability to distinguish between classes across all classification thresholds.

## Input / output format

**Input**: High-dimensional tabular feature vectors extracted from PE files (static malware features).

**Output**: Binary classification label (malware/benign) or probability scores for each class.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred, y_prob=None):
    accuracy = (y_true == y_pred).mean()
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)
    f1 = f1_score(y_true, y_pred)
    auc = roc_auc_score(y_true, y_prob) if y_prob is not None else None
    return {'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1': f1, 'auc': auc}
```

## Common pitfalls

- KNN performance heavily degrades in high-dimensional spaces due to distance distortion, requiring dimensionality reduction.
- LDA significantly harms tree-based ensemble models by compressing non-linear decision boundaries into a single axis.
- TabNet is highly sensitive to feature compression and may underperform under PCA without extensive tuning.
- The evaluation only covers static features and does not test adversarial evasion or concept drift over time.

## Evidence (verbatim from paper)

> We evaluate classification accuracy, precision, recall, F1-score, and AUC to understand the models' generalizability, robustness, and discriminative capacity.

## Citation

```bibtex
@misc{abedin2025evaluating,
  title={Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset},
  author={Abedin et al. (2025)},
  year={2025},
  note={arXiv:2507.16952}
}
```

- arXiv: 2507.16952