# Nslkdd Fetfids Eval

> Evaluates a federated transformer-based intrusion detection model on network traffic data to classify benign and malicious packets across five attack categories under a realistic class-imbalanced, distributed setting. Use when the user wants to benchmark on NSLKDD, or asks about evaluating this task. Reports detection performance.

- Skill: `qhjqhj00/nslkdd-fetfids-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/nslkdd-fetfids-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/nslkdd-fetfids-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/nslkdd-fetfids-eval

---


# nslkdd-fetfids-eval

> FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm — Ghosh et al. (2025) (arXiv:2508.09056, 2025)

## What this evaluates

Evaluates a federated transformer-based intrusion detection model on network traffic data to classify benign and malicious packets across five attack categories under a realistic class-imbalanced, distributed setting.

## Datasets

- **NSLKDD** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `detection performance` **(primary)** — range: percent
  - Not explicitly defined in the provided text; typically refers to classification accuracy or F1-score in intrusion detection contexts.

## Input / output format

**Input**: 41-feature network traffic packet records

**Output**: Classification into one of 5 classes: Benign, DoS, U2R, R2L, or Probing

## Scoring recipe

```python
# Not specified in provided text. Standard classification accuracy is assumed:
# def score(preds, gold):
#     return sum(p == g for p, g in zip(preds, gold)) / len(gold)
```

## Common pitfalls

- Class imbalance is intentionally preserved across federated devices to emulate realistic edge scenarios, so data augmentation is not applied.
- The dataset is split into fixed percentages (85/7.5/7.5) rather than using the original NSL-KDD fixed train/test splits.

## Evidence (verbatim from paper)

> achieving superior detection performance in edge-based federated learning settings by focusing on attack-specific feature patterns rather than positional context.

## Citation

```bibtex
@misc{ghosh2025fetfids,
  title={FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm},
  author={Ghosh et al. (2025)},
  year={2025},
  note={arXiv:2508.09056}
}
```

- arXiv: 2508.09056