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selfconstruct3d/AttackGroup-MPNET

sourceHugging Faceupdated 11mo agoView on Hugging Face

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Model Card

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)

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 ("mpnetmlmcyber_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.

—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

—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.