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
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)
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)
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
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.
- 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.
Model Card Authors
- Dženan Hamzić
Model Card Contact
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Model tree for selfconstruct3d/AttackGroup-MPNET
Base model
sentence-transformers/all-mpnet-base-v2