Nsll Kdd Hdc Eval

---
name: nsll-kdd-hdc-eval
description: Evaluates the ability of a hyperdimensional computing framework to detect and classify network intrusions in IoT environments. It probes the model's capacity to encode high-dimensional feature vectors, learn class prototypes, and accurately distinguish between normal traffic and specific attack types (DoS, probe, R2L, U2R). Use when the user wants to benchmark on NSL-KDD, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2503.03037
  bibtex_key: [REDACTED]
  confidence: high
---

# nsll-kdd-hdc-eval

> Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset — Ghazal Ghajari et al. (2025) (arXiv:2503.03037, 2025)

## What this evaluates

Evaluates the ability of a hyperdimensional computing framework to detect and classify network intrusions in IoT environments. It probes the model's capacity to encode high-dimensional feature vectors, learn class prototypes, and accurately distinguish between normal traffic and specific attack types (DoS, probe, R2L, U2R).

## Datasets

- **NSL-KDD** — total ?; splits: train (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - The proportion of correctly classified samples out of the total number of test samples, calculated as (correct predictions / total predictions) * 100. Reported as a percentage.

## Input / output format

**Input**: A network traffic sample represented as a vector of numerical features. Each feature is mapped to a discrete bin, and the entire sample is transformed into a 10,000-dimensional binary hypervector via XOR-based encoding and binarization.

**Output**: A single class label from the set {Normal, DoS, Probe, R2L, U2R}, assigned by selecting the class representative with the highest cosine similarity to the sample's hypervector.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / len(gold_labels)) * 100
```

## Common pitfalls

- The binarization threshold T is typically set to half the number of features, but sensitivity to this threshold can significantly alter hypervector sparsity and classification boundaries.
- Iterative training updates (50 iterations with learning rate alpha) are applied to class representatives; stopping early or choosing an inappropriate alpha can lead to suboptimal convergence and lower accuracy.
- Cosine similarity is computed on binarized hypervectors rather than raw continuous vectors, which changes the geometric interpretation of similarity compared to standard HDC implementations.

## Evidence (verbatim from paper)

> In comparison to these methods, the proposed approach using hyperdimensional computing-based classification achieved a remarkable 99.5% accuracy on the NSL-KDD dataset, as shown in Table I.

## Citation

```bibtex
@misc{ghajari2025intrusion,
  title={Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset},
  author={Ghazal Ghajari et al. (2025)},
  year={2025},
  note={arXiv:2503.03037}
}
```

- arXiv: 2503.03037