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--- |
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library_name: transformers |
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tags: |
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- cybersecurity |
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- mpnet |
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- classification |
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- fine-tuned |
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license: creativeml-openrail-m |
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language: |
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- en |
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base_model: |
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- sentence-transformers/all-mpnet-base-v2 |
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--- |
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# AttackGroup-MPNET - Model Card for MPNet Cybersecurity Classifier |
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This is a fine-tuned MPNet model specialized for classifying cybersecurity threat groups based on textual descriptions of their tactics and techniques. |
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## Model Details |
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### Model Description |
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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). |
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- **Developed by:** Dženan Hamzić |
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- **Model type:** Transformer-based classification model (MPNet) |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** microsoft/mpnet-base (with intermediate MLM fine-tuning) |
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### Model Sources |
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- **Base Model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) |
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## Uses |
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### Direct Use |
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This model classifies textual cybersecurity descriptions into known cybersecurity threat groups. |
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### Downstream Use |
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Integration into Cyber Threat Intelligence platforms, SOC incident analysis tools, and automated threat detection systems. |
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### Out-of-Scope Use |
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- General language tasks unrelated to cybersecurity |
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- Tasks outside the cybersecurity domain |
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## Bias, Risks, and Limitations |
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This model specializes in cybersecurity contexts. Predictions for unrelated contexts may be inaccurate. |
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### Recommendations |
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Always verify predictions with cybersecurity analysts before using in critical decision-making scenarios. |
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## How to Get Started with the Model (Classification) |
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```python |
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import torch |
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import torch.nn as nn |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch.optim as optim |
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import numpy as np |
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from huggingface_hub import hf_hub_download |
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import json |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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label_to_groupid_file = hf_hub_download( |
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repo_id="selfconstruct3d/AttackGroup-MPNET", |
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filename="label_to_groupid.json" |
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) |
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with open(label_to_groupid_file, "r") as f: |
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label_to_groupid = json.load(f) |
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# Load explicitly your fine-tuned MPNet model |
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classifier_model = AutoModelForSequenceClassification.from_pretrained("selfconstruct3d/AttackGroup-MPNET", num_labels=len(label_to_groupid)).to(device) |
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# Load explicitly your tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") |
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def predict_group(sentence): |
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classifier_model.eval() |
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encoding = tokenizer( |
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sentence, |
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truncation=True, |
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padding="max_length", |
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max_length=128, |
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return_tensors="pt" |
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) |
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input_ids = encoding["input_ids"].to(device) |
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attention_mask = encoding["attention_mask"].to(device) |
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with torch.no_grad(): |
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outputs = classifier_model(input_ids=input_ids, attention_mask=attention_mask) |
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logits = outputs.logits |
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predicted_label = torch.argmax(logits, dim=1).cpu().item() |
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predicted_groupid = label_to_groupid[str(predicted_label)] |
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return predicted_groupid |
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# Example usage explicitly: |
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sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
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predicted_class = predict_group(sentence) |
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print(f"Predicted GroupID: {predicted_class}") |
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``` |
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Predicted GroupID: G0001 |
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https://attack.mitre.org/groups/G0001/ |
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## How to Get Started with the Model (Embeddings) |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from huggingface_hub import hf_hub_download |
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import json |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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label_to_groupid_file = hf_hub_download( |
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repo_id="selfconstruct3d/AttackGroup-MPNET", |
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filename="label_to_groupid.json" |
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) |
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with open(label_to_groupid_file, "r") as f: |
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label_to_groupid = json.load(f) |
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# Load your fine-tuned classification model |
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model_name = "selfconstruct3d/AttackGroup-MPNET" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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classifier_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(label_to_groupid)).to(device) |
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def get_embedding(sentence): |
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classifier_model.eval() |
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encoding = tokenizer( |
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sentence, |
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truncation=True, |
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padding="max_length", |
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max_length=128, |
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return_tensors="pt" |
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) |
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input_ids = encoding["input_ids"].to(device) |
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attention_mask = encoding["attention_mask"].to(device) |
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with torch.no_grad(): |
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outputs = classifier_model.mpnet(input_ids=input_ids, attention_mask=attention_mask) |
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cls_embedding = outputs.last_hidden_state[:, 0, :].cpu().numpy().flatten() |
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return cls_embedding |
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# Example explicitly: |
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sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
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embedding = get_embedding(sentence) |
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print("Embedding shape:", embedding.shape) |
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print("Embedding values:", embedding) |
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``` |
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## Training Details |
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### Training Data |
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To be anounced... |
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### Training Procedure |
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- Fine-tuned from: MLM fine-tuned MPNet ("mpnet_mlm_cyber_finetuned-v2") |
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- Epochs: 32 |
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- Learning rate: 5e-6 |
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- Batch size: 16 |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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- **Testing Data:** Stratified sample from original dataset. |
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- **Metrics:** Accuracy, Weighted F1 Score |
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### Results |
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| Metric | Value | |
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|------------------------|---------| |
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| Cl. Accuracy (Test) | 0.9564 | |
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| W. F1 Score (Test) | 0.9577 | |
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## Evaluation Results |
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| Model | Accuracy | F1 Macro | F1 Weighted | Embedding Variability | |
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|-----------------------|----------|----------|-------------|-----------------------| |
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| **AttackGroup-MPNET** | **0.85** | **0.759**| **0.847** | 0.234 | |
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| GTE Large | 0.66 | 0.571 | 0.667 | 0.266 | |
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| E5 Large v2 | 0.64 | 0.541 | 0.650 | 0.355 | |
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| Original MPNet | 0.63 | 0.534 | 0.619 | 0.092 | |
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| BGE Large | 0.53 | 0.418 | 0.519 | 0.366 | |
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| SupSimCSE | 0.50 | 0.373 | 0.479 | 0.227 | |
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| MLM Fine-tuned MPNet | 0.44 | 0.272 | 0.411 | 0.125 | |
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| SecBERT | 0.41 | 0.315 | 0.410 | 0.591 | |
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| SecureBERT_Plus | 0.36 | 0.252 | 0.349 | 0.267 | |
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| CySecBERT | 0.34 | 0.235 | 0.323 | 0.229 | |
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| ATTACK-BERT | 0.33 | 0.240 | 0.316 | 0.096 | |
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| Secure_BERT | 0.00 | 0.000 | 0.000 | 0.007 | |
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| CyBERT | 0.00 | 0.000 | 0.000 | 0.015 | |
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| Model | Similarity Search Recall@5 | Few-shot Accuracy | In-dist Similarity | OOD Similarity | Robustness Similarity | |
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|----------------------|----------------------------|-------------------|--------------------|----------------|-----------------------| |
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| **AttackGroup-MPNET**| **0.934** | **0.857** | 0.235 | 0.017 | 0.948 | |
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| Original MPNet | 0.786 | 0.643 | 0.217 | -0.004 | 0.941 | |
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| E5 Large v2 | 0.778 | 0.679 | 0.727 | 0.013 | 0.977 | |
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| GTE Large | 0.746 | 0.786 | 0.845 | 0.002 | 0.984 | |
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| BGE Large | 0.632 | 0.750 | 0.533 | -0.006 | 0.970 | |
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| SupSimCSE | 0.616 | 0.571 | 0.683 | -0.015 | 0.978 | |
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| SecBERT | 0.468 | 0.429 | 0.586 | -0.001 | 0.970 | |
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| CyBERT | 0.452 | 0.250 | 1.000 | -0.001 | 1.000 | |
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| ATTACK-BERT | 0.362 | 0.571 | 0.157 | -0.005 | 0.950 | |
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| CySecBERT | 0.424 | 0.500 | 0.734 | -0.015 | 0.954 | |
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| Secure_BERT | 0.424 | 0.250 | 0.990 | 0.050 | 0.998 | |
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| SecureBERT_Plus | 0.406 | 0.464 | 0.981 | 0.040 | 0.998 | |
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### Single Prediction Example |
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```python |
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import torch |
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import torch.nn as nn |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch.optim as optim |
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import numpy as np |
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from huggingface_hub import hf_hub_download |
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import json |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load explicitly your fine-tuned MPNet model |
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classifier_model = AutoModelForSequenceClassification.from_pretrained("selfconstruct3d/AttackGroup-MPNET").to(device) |
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# Load explicitly your tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") |
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label_to_groupid_file = hf_hub_download( |
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repo_id="selfconstruct3d/AttackGroup-MPNET", |
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filename="label_to_groupid.json" |
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) |
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with open(label_to_groupid_file, "r") as f: |
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label_to_groupid = json.load(f) |
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def predict_group(sentence): |
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classifier_model.eval() |
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encoding = tokenizer( |
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sentence, |
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truncation=True, |
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padding="max_length", |
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max_length=128, |
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return_tensors="pt" |
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) |
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input_ids = encoding["input_ids"].to(device) |
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attention_mask = encoding["attention_mask"].to(device) |
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with torch.no_grad(): |
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outputs = classifier_model(input_ids=input_ids, attention_mask=attention_mask) |
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logits = outputs.logits |
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predicted_label = torch.argmax(logits, dim=1).cpu().item() |
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predicted_groupid = label_to_groupid[str(predicted_label)] |
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return predicted_groupid |
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# Example usage explicitly: |
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sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
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predicted_class = predict_group(sentence) |
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print(f"Predicted GroupID: {predicted_class}") |
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``` |
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## Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
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- **Hardware Type:** [To be filled by user] |
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- **Hours used:** [To be filled by user] |
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- **Cloud Provider:** [To be filled by user] |
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- **Compute Region:** [To be filled by user] |
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- **Carbon Emitted:** [To be filled by user] |
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## Technical Specifications |
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### Model Architecture |
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- MPNet architecture with classification head (768 -> 512 -> num_labels) |
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- Last 10 transformer layers fine-tuned explicitly |
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## Environmental Impact |
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Carbon emissions should be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
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## Model Card Authors |
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- Dženan Hamzić |
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## Model Card Contact |
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- https://www.linkedin.com/in/dzenan-hamzic/ |
