Added PDF detection malware

Detection Models/PDF_Malware_Detection/README.md

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+# PDF Malware Detection
+
+This project implements a machine learning-based system for detecting potential malware in PDF files. It includes feature extraction from PDF files, model training, and a prediction script for classifying PDFs as potentially malicious or benign.
+
+## Components
+
+1. **Feature Extraction** (`pdf_feature_extraction.py`)
+   - Extracts various features from PDF files using PyMuPDF and pdfid.
+   - Features include metadata, structural elements, and presence of potentially risky elements.
+
+2. **Model Training** (`pdf_malware_dataset_training.py`)
+   - Prepares the dataset, handles data cleaning and preprocessing.
+   - Trains a Random Forest classifier for malware detection.
+   - Includes code for hyperparameter tuning (commented out).
+
+3. **Prediction Script** (`predict_malware.py`)
+   - Uses the trained model to predict whether a given PDF file is potentially malicious.
+
+## Setup
+
+1. Install required dependencies:
+   ```
+   pip install numpy pandas matplotlib scikit-learn imblearn PyMuPDF pdfid joblib
+   ```
+
+2. Ensure you have the dataset file `PDFMalware2022.csv` in the `Dataset` folder.
+
+## Usage
+
+### Training the Model
+
+1. Run the `pdf_malware_dataset_training.py` script to train the model:
+   ```
+   python pdf_malware_dataset_training.py
+   ```
+   This will create a `random_forest_model.pkl` file containing the trained model.
+
+### Predicting Malware
+
+For prediction run the script directly along with path to pdf file:
+   ```
+   python predict_malware.py path/to/your/pdf_file.pdf
+   ```
+
+## Note
+
+This project is for educational and research purposes only. It should not be used as a sole means of determining file safety. Always use caution when dealing with potentially malicious files and consult with cybersecurity professionals for comprehensive security measures.
+
+## Future Improvements
+
+- Implement more advanced feature extraction techniques.
+- Explore other machine learning algorithms for potentially better performance.
+- Add a user-friendly interface for easier interaction with the prediction system.
+- Incorporate regular model updates with new malware samples to keep the detection current.