selfconstruct3d
/

mpnet-classification-finetuned-cyber-groups

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- cybersecurity
+- mpnet
+- embeddings
+- classification
+license: apache-2.0
+language:
+- en
+base_model:
+- microsoft/mpnet-base
 ---
+# MPNet (Cyber) - MPNet Fine-Tuned for Cybersecurity Group Classification
+This MPNet model was fine-tuned specifically for classifying cybersecurity threat groups based on textual descriptions from cybersecurity reports.
 ## Model Details
 ### Model Description
+This model is based on `microsoft/mpnet-base` and fine-tuned using Masked Language Modeling (MLM) and supervised classification on cybersecurity threat intelligence descriptions, primarily focused on known threat actor groups.
+### Model Information
+- **Base Model:** microsoft/mpnet-base
+- **Tasks:** Text classification, embedding generation
+- **Language:** English
+## Intended Use
+### Primary Use
+This model generates specialized embeddings that are useful for:
+- Identifying cybersecurity threat actor groups from textual descriptions
+- Cybersecurity threat intelligence analysis
+- Embedding-based retrieval tasks in cybersecurity contexts
 ### Out-of-Scope Use
+This model is not intended for general language tasks outside cybersecurity contexts.
+## Performance Evaluation
+The model was benchmarked against state-of-the-art cybersecurity NLP models:
+| Model            | Classification Accuracy | Embedding Variability |
+|------------------|-------------------------|-----------------------|
+| Original MPNet   | 55.73%                  | 0.0798                |
+| SecBERT          | 91.67%                  | 0.5911                |
+| ATTACK-BERT      | 83.51%                  | 0.0960                |
+| MPNet (Cyber)    | 72.74%                  | 0.1239                |
+| SecureBERT       | 49.31%                  | 0.0071                |
+### Downstream Tasks
+- Attribution of cybersecurity incidents
+- Automated analysis of threat intelligence reports
+- Embeddings for cybersecurity threat detection
+### Limitations
+- Best suited for English language cybersecurity contexts
+- May require further fine-tuning for highly specific tasks
+## Usage
+To use this model:
+```python
+from transformers import AutoTokenizer, MPNetModel
+import torch
+tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d
+/
+mpnet-classification-finetuned-cyber-groups ")
+model = MPNetModel.from_pretrained("selfconstruct3d
+/
+mpnet-classification-finetuned-cyber-groups ")
+inputs = tokenizer("APT38 uses ransomware for financial gains.", return_tensors="pt")
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state.mean(dim=1)
+```
 ## Training Details
 ### Training Data
+Fine-tuned on descriptions of threat actor activities sourced from cybersecurity reports, including MITRE ATT&CK techniques.
+### Hyperparameters
+- **Epochs:** 10 (MLM), 20 (classification)
+- **Batch size:** 16
+- **Learning rate:** 5e-6 (MLM), 2e-6 (classification)
+- **Hardware:** GPU (CUDA-enabled)
+## Citation
+If using this model, please cite as:
+```bibtex
+@misc{mpnet_cyber_finetune,
+  author = {Hamzić, D.},
+  title = {MPNet Fine-Tuned for Cybersecurity Group Classification},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/selfconstruct3d/mpnet-classification-finetuned-cyber-groups}
+}
+```
+## Contact
+- **Author:** Džemal Hamzić
+- **Contact Information:** [Your Contact Information]