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--- |
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tags: |
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- phishing-detection |
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- logistic-regression |
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- tfidf |
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- sklearn |
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- datasets |
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- huggingface |
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license: mit |
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--- |
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# Phishing Detection Model using Logistic Regression and TF-IDF |
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This model is a phishing detection classifier built using TF-IDF for feature extraction and Logistic Regression for classification. It processes text data to identify phishing attempts with high accuracy. |
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## Model Details |
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- **Framework**: Scikit-learn |
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- **Feature Extraction**: TF-IDF Vectorizer (top 5000 features) |
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- **Algorithm**: Logistic Regression |
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- **Dataset**: [ealvaradob/phishing-dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset) (combined_reduced subset) |
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## Installation |
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Before using the model, ensure you have the necessary dependencies installed: |
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```bash |
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pip install scikit-learn |
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pip install -U "tensorflow-text==2.13.*" |
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pip install "tf-models-official==2.13.*" |
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pip uninstall -y pyarrow datasets |
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pip install pyarrow datasets |
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``` |
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## How to Use |
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Below is an example of how to train and evaluate the model: |
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```python |
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from datasets import load_dataset |
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from sklearn.feature_extraction.text import TfidfVectorizer |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import accuracy_score |
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# Load the dataset |
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dataset_reduced = load_dataset("ealvaradob/phishing-dataset", "combined_reduced", trust_remote_code=True) |
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# Convert to pandas DataFrame |
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df = dataset_reduced['train'].to_pandas() |
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# Extract text and labels |
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text = df['text'].values |
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labels = df['label'].values |
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# Split the data into train and test sets |
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train_text, test_text, train_labels, test_labels = train_test_split( |
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text, labels, test_size=0.2, random_state=42 |
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) |
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# Create and fit the TF-IDF vectorizer |
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vectorizer = TfidfVectorizer(max_features=5000) |
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vectorizer.fit(train_text) |
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# Transform the text data into numerical features |
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train_features = vectorizer.transform(train_text) |
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test_features = vectorizer.transform(test_text) |
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# Create and train the logistic regression model |
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model = LogisticRegression() |
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model.fit(train_features, train_labels) |
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# Make predictions on the test set |
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predictions = model.predict(test_features) |
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# Evaluate the model's accuracy |
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accuracy = accuracy_score(test_labels, predictions) |
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print(f'Accuracy: {accuracy}') |
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``` |
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## Results |
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- **Accuracy**: The model achieves an accuracy of `{{accuracy}}` on the test set. |
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## Dataset |
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The dataset used for training and evaluation is the [ealvaradob/phishing-dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset). It contains a variety of phishing and non-phishing samples labeled as `1` (phishing) and `0` (non-phishing). |
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## Limitations and Future Work |
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- The model uses a simple Logistic Regression algorithm, which may not capture complex patterns in text as effectively as deep learning models. |
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- Future versions could incorporate advanced NLP techniques like BERT or transformer-based models. |
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## License |
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This project is licensed under the MIT License. Feel free to use, modify, and distribute this model as per the terms of the license. |
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## Acknowledgements |
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- [Hugging Face Datasets](https://huggingface.co/datasets) |
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- [Scikit-learn](https://scikit-learn.org/) |
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--- |
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license: apache-2.0 |
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--- |
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