---
library_name: transformers
tags:
- cybersecurity
- mpnet
- classification
- fine-tuned
---

# Model Card for MPNet Cybersecurity Classifier

This is a fine-tuned MPNet model specialized for classifying cybersecurity threat groups based on textual descriptions of their tactics and techniques.

## Model Details

### Model Description

This model is a fine-tuned MPNet classifier specialized in categorizing cybersecurity threat groups based on textual descriptions of their tactics, techniques, and procedures (TTPs).

- **Developed by:** Dženan Hamzić
- **Model type:** Transformer-based classification model (MPNet)
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** microsoft/mpnet-base (with intermediate MLM fine-tuning)

### Model Sources

- **Base Model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base)

## Uses

### Direct Use

This model classifies textual cybersecurity descriptions into known cybersecurity threat groups.

### Downstream Use

Integration into Cyber Threat Intelligence platforms, SOC incident analysis tools, and automated threat detection systems.

### Out-of-Scope Use

- General language tasks unrelated to cybersecurity
- Tasks outside the cybersecurity domain

## Bias, Risks, and Limitations

This model specializes in cybersecurity contexts. Predictions for unrelated contexts may be inaccurate.

### Recommendations

Always verify predictions with cybersecurity analysts before using in critical decision-making scenarios.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, MPNetModel
import torch

model_name = "mpnet_classification_finetuned_v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = MPNetModel.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Example inference
sentence = "APT38 has used phishing emails with malicious links to distribute malware."
inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding="max_length", max_length=128).to(device)

with torch.no_grad():
    outputs = model(**inputs)
    cls_embedding = outputs.last_hidden_state[:, 0, :]
    predicted_class = classifier_model.classifier(cls_embedding).argmax(dim=1).cpu().item()

print(f"Predicted GroupID: {predicted_class}")
```

## Training Details

### Training Data

The training dataset comprises balanced textual descriptions of various cybersecurity threat groups' TTPs, augmented through synonym replacement to increase diversity.

### Training Procedure

- Fine-tuned from: MLM fine-tuned MPNet ("mpnet_mlm_cyber_finetuned-v2")
- Epochs: 20
- Learning rate: 5e-6
- Batch size: 16

## Evaluation

### Testing Data, Factors & Metrics

- **Testing Data:** Stratified sample from original dataset.
- **Metrics:** Accuracy, Weighted F1 Score

### Results

| Metric                 | Value   |
|------------------------|---------|
| Classification Accuracy (Test) | 0.7161 |
| Weighted F1 Score      | [More Information Needed] |

### Single Prediction Example

```python

# Create explicit mapping from numeric labels to original GroupIDs
label_to_groupid = dict(enumerate(train_df["GroupID"].astype("category").cat.categories))

def predict_group(sentence):
    classifier_model.eval()
    encoding = tokenizer(
        sentence,
        truncation=True,
        padding="max_length",
        max_length=128,
        return_tensors="pt"
    )
    input_ids = encoding["input_ids"].to(device)
    attention_mask = encoding["attention_mask"].to(device)

    with torch.no_grad():
        logits = classifier_model(input_ids, attention_mask)
        predicted_label = torch.argmax(logits, dim=1).cpu().item()


    # Explicitly convert numeric label to original GroupID
    predicted_groupid = label_to_groupid[predicted_label]
    return predicted_groupid

sentence = "APT38 has used phishing emails with malicious links to distribute malware."
predicted_class = predict_group(sentence)
print(f"Predicted GroupID: {predicted_class}")  # e.g., Predicted GroupID: G0081
```

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).

- **Hardware Type:** [To be filled by user]
- **Hours used:** [To be filled by user]
- **Cloud Provider:** [To be filled by user]
- **Compute Region:** [To be filled by user]
- **Carbon Emitted:** [To be filled by user]

## Technical Specifications

### Model Architecture

- MPNet architecture with classification head (768 -> 512 -> num_labels)
- Last 10 transformer layers fine-tuned explicitly

## Environmental Impact

Carbon emissions should be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).

## Model Card Authors

- Dženan Hamzić

## Model Card Contact

- [More Information Needed]