| --- |
| library_name: transformers |
| tags: |
| - cybersecurity |
| - mpnet |
| - classification |
| - fine-tuned |
| language: |
| - en |
| base_model: |
| - sentence-transformers/all-mpnet-base-v2 |
| --- |
| |
| # AttackGroup-MPNET - 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 (Classification) |
|
|
| ```python |
| import torch |
| import torch.nn as nn |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch.optim as optim |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
| import json |
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| |
| label_to_groupid_file = hf_hub_download( |
| repo_id="selfconstruct3d/AttackGroup-MPNET", |
| filename="label_to_groupid.json" |
| ) |
| |
| with open(label_to_groupid_file, "r") as f: |
| label_to_groupid = json.load(f) |
| |
| # Load explicitly your fine-tuned MPNet model |
| classifier_model = AutoModelForSequenceClassification.from_pretrained("selfconstruct3d/AttackGroup-MPNET", num_labels=len(label_to_groupid)).to(device) |
| |
| # Load explicitly your tokenizer |
| tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") |
| |
| 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(): |
| outputs = classifier_model(input_ids=input_ids, attention_mask=attention_mask) |
| logits = outputs.logits |
| predicted_label = torch.argmax(logits, dim=1).cpu().item() |
| |
| predicted_groupid = label_to_groupid[str(predicted_label)] |
| return predicted_groupid |
| |
| # Example usage explicitly: |
| sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
| predicted_class = predict_group(sentence) |
| print(f"Predicted GroupID: {predicted_class}") |
| ``` |
| Predicted GroupID: G0001 |
| https://attack.mitre.org/groups/G0001/ |
|
|
|
|
| ## How to Get Started with the Model (Embeddings) |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| from huggingface_hub import hf_hub_download |
| import json |
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| |
| label_to_groupid_file = hf_hub_download( |
| repo_id="selfconstruct3d/AttackGroup-MPNET", |
| filename="label_to_groupid.json" |
| ) |
| |
| with open(label_to_groupid_file, "r") as f: |
| label_to_groupid = json.load(f) |
| |
| |
| # Load your fine-tuned classification model |
| model_name = "selfconstruct3d/AttackGroup-MPNET" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| classifier_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(label_to_groupid)).to(device) |
| |
| def get_embedding(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(): |
| outputs = classifier_model.mpnet(input_ids=input_ids, attention_mask=attention_mask) |
| cls_embedding = outputs.last_hidden_state[:, 0, :].cpu().numpy().flatten() |
| |
| return cls_embedding |
| |
| # Example explicitly: |
| sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
| embedding = get_embedding(sentence) |
| print("Embedding shape:", embedding.shape) |
| print("Embedding values:", embedding) |
| ``` |
|
|
|
|
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| To be anounced... |
|
|
| ### Training Procedure |
|
|
| - Fine-tuned from: MLM fine-tuned MPNet ("mpnet_mlm_cyber_finetuned-v2") |
| - Epochs: 32 |
| - 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 | |
| |------------------------|---------| |
| | Cl. Accuracy (Test) | 0.9564 | |
| | W. F1 Score (Test) | 0.9577 | |
| |
| |
| ## Evaluation Results |
| |
| | Model | Accuracy | F1 Macro | F1 Weighted | Embedding Variability | |
| |-----------------------|----------|----------|-------------|-----------------------| |
| | **AttackGroup-MPNET** | **0.85** | **0.759**| **0.847** | 0.234 | |
| | GTE Large | 0.66 | 0.571 | 0.667 | 0.266 | |
| | E5 Large v2 | 0.64 | 0.541 | 0.650 | 0.355 | |
| | Original MPNet | 0.63 | 0.534 | 0.619 | 0.092 | |
| | BGE Large | 0.53 | 0.418 | 0.519 | 0.366 | |
| | SupSimCSE | 0.50 | 0.373 | 0.479 | 0.227 | |
| | MLM Fine-tuned MPNet | 0.44 | 0.272 | 0.411 | 0.125 | |
| | SecBERT | 0.41 | 0.315 | 0.410 | 0.591 | |
| | SecureBERT_Plus | 0.36 | 0.252 | 0.349 | 0.267 | |
| | CySecBERT | 0.34 | 0.235 | 0.323 | 0.229 | |
| | ATTACK-BERT | 0.33 | 0.240 | 0.316 | 0.096 | |
| | Secure_BERT | 0.00 | 0.000 | 0.000 | 0.007 | |
| | CyBERT | 0.00 | 0.000 | 0.000 | 0.015 | |
| |
| |
| | Model | Similarity Search Recall@5 | Few-shot Accuracy | In-dist Similarity | OOD Similarity | Robustness Similarity | |
| |----------------------|----------------------------|-------------------|--------------------|----------------|-----------------------| |
| | **AttackGroup-MPNET**| **0.934** | **0.857** | 0.235 | 0.017 | 0.948 | |
| | Original MPNet | 0.786 | 0.643 | 0.217 | -0.004 | 0.941 | |
| | E5 Large v2 | 0.778 | 0.679 | 0.727 | 0.013 | 0.977 | |
| | GTE Large | 0.746 | 0.786 | 0.845 | 0.002 | 0.984 | |
| | BGE Large | 0.632 | 0.750 | 0.533 | -0.006 | 0.970 | |
| | SupSimCSE | 0.616 | 0.571 | 0.683 | -0.015 | 0.978 | |
| | SecBERT | 0.468 | 0.429 | 0.586 | -0.001 | 0.970 | |
| | CyBERT | 0.452 | 0.250 | 1.000 | -0.001 | 1.000 | |
| | ATTACK-BERT | 0.362 | 0.571 | 0.157 | -0.005 | 0.950 | |
| | CySecBERT | 0.424 | 0.500 | 0.734 | -0.015 | 0.954 | |
| | Secure_BERT | 0.424 | 0.250 | 0.990 | 0.050 | 0.998 | |
| | SecureBERT_Plus | 0.406 | 0.464 | 0.981 | 0.040 | 0.998 | |
| |
| |
| |
| ### Single Prediction Example |
| |
| ```python |
| |
| import torch |
| import torch.nn as nn |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch.optim as optim |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
| import json |
|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| # Load explicitly your fine-tuned MPNet model |
| classifier_model = AutoModelForSequenceClassification.from_pretrained("selfconstruct3d/AttackGroup-MPNET").to(device) |
| |
| # Load explicitly your tokenizer |
| tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") |
|
|
|
|
| label_to_groupid_file = hf_hub_download( |
| repo_id="selfconstruct3d/AttackGroup-MPNET", |
| filename="label_to_groupid.json" |
| ) |
| |
| with open(label_to_groupid_file, "r") as f: |
| label_to_groupid = json.load(f) |
| |
| 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(): |
| outputs = classifier_model(input_ids=input_ids, attention_mask=attention_mask) |
| logits = outputs.logits |
| predicted_label = torch.argmax(logits, dim=1).cpu().item() |
| |
| predicted_groupid = label_to_groupid[str(predicted_label)] |
| return predicted_groupid |
| |
| # Example usage explicitly: |
| sentence = "APT38 has used phishing emails with malicious links to distribute malware." |
| predicted_class = predict_group(sentence) |
| print(f"Predicted GroupID: {predicted_class}") |
| ``` |
| |
| ## 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 |
|
|
| - https://www.linkedin.com/in/dzenan-hamzic/ |
|
|
|
|
| ## Licence |
| This model is licensed for non-commercial use only (CC BY-NC 4.0). |
| For commercial inquiries, please contact dzenan.hamzic@ait.ac.at. |
