# Malware Detection Eval

> Binary classification of software binaries as benign or malicious based on their control flow graphs. It probes the model's ability to learn graph-structured representations and route them through specialized experts for accurate detection. Use when the user wants to benchmark on BODMAS, DikeDataset, PMML, or asks about evaluating this task. Reports Accuracy.

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

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# malware-detection-eval

> Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection — Shokouhinejad et al. (2026) (arXiv:2602.19025, 2026)

## What this evaluates

Binary classification of software binaries as benign or malicious based on their control flow graphs. It probes the model's ability to learn graph-structured representations and route them through specialized experts for accurate detection.

## Datasets

- **BODMAS** — total 122; splits: train (-1), test (-1)
- **DikeDataset** — total 319; splits: train (-1), test (-1)
- **PMML** — total 390; splits: train (-1), test (-1)

## Metrics

- `Accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly classified samples out of the total number of samples. Calculated as (TP + TN) / Total.
- `F1` — range: [0, 1]
  - Harmonic mean of precision and recall. Calculated as 2 * (Precision * Recall) / (Precision + Recall). Reported separately for benign and malicious classes.

## Input / output format

**Input**: Control flow graphs (CFGs) extracted from binaries using angr, with node features compressed to 64 dimensions via a symmetric autoencoder.

**Output**: Binary classification label (Benign or Malware) per graph, plus routing gate weights and expert selections for MoE variants.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    correct = sum(1 for t, p in zip(y_true, y_pred) if t == p)
    accuracy = correct / len(y_true)
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
    return {'accuracy': accuracy, 'f1': f1}
```

## Common pitfalls

- Datasets have highly imbalanced class distributions (only DikeDataset is benign, BODMAS/PMML are malware), requiring careful stratified splitting to preserve class proportions.
- Node features are heavily preprocessed (439D to 64D via autoencoder); evaluating raw features without this compression will yield different results.
- Explainability metrics (Fidelity+/Fidelity-) are evaluated across sparsity levels (5%-95%), not just a single threshold.

## Evidence (verbatim from paper)

> The dataset was stratified and split into 80% for training and 20% for testing, preserving equal class proportions for benign and malicious samples in both subsets. Table[2] reports classwise precision, recall, F1, and overall accuracy for all models.

## Citation

```bibtex
@misc{shokouhinejad2026routing,
  title={Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection},
  author={Shokouhinejad et al. (2026)},
  year={2026},
  note={arXiv:2602.19025}
}
```

- arXiv: 2602.19025