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--- |
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license: apache-2.0 |
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language: |
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- en |
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- de |
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--- |
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# 🛡️ MLP Cybersecurity Classifier |
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This repository hosts a lightweight `scikit-learn`-based MLP classifier trained to distinguish cybersecurity-related content from other text, using sentence-transformer embeddings. It supports English and German input texts. |
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## 📊 Training Data |
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The model was trained on a multilingual dataset of cybersecurity and non-cybersecurity news articles. The dataset is publicly available on Zenodo: |
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🔗 [https://zenodo.org/records/16417939](https://zenodo.org/records/16417939) |
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## 📦 Model Details |
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- **Architecture**: `MLPClassifier` with hidden layers `(128, 64)` |
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- **Embedding model**: [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) |
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- **Input**: Cleaned article (removed stopwords) or report text |
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- **Output**: Binary label (e.g., `Cybersecurity`, `Not Cybersecurity`) |
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- **Languages**: English, German |
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## 🔧 Usage |
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```python |
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from sentence_transformers import SentenceTransformer |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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import pandas as pd |
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import joblib |
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from huggingface_hub import hf_hub_download |
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# Load your cleaned dataset |
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df = pd.read_csv("your_dataset.csv") # Requires 'clean_text' and 'label' columns |
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# Load the sentence transformer |
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embedder = SentenceTransformer("intfloat/multilingual-e5-large") |
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# Train-test split |
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X_train, X_test, y_train, y_test = train_test_split( |
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df["clean_text"], |
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df["label"], |
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test_size=0.05, |
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stratify=df["label"], |
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random_state=42 |
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) |
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# Encode labels |
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label_encoder = LabelEncoder() |
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y_train_enc = label_encoder.fit_transform(y_train) |
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y_test_enc = label_encoder.transform(y_test) |
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# Generate sentence embeddings |
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X_train_emb = embedder.encode(X_train.tolist(), convert_to_numpy=True, show_progress_bar=True) |
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X_test_emb = embedder.encode(X_test.tolist(), convert_to_numpy=True, show_progress_bar=True) |
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# Load the trained classifier |
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model_path = hf_hub_download(repo_id="selfconstruct3d/cybersec_classifier", filename="cybersec_classifier.pkl") |
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model = joblib.load(model_path) |
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# Predict |
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y_pred = model.predict(X_test_emb) |
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