🧼 SSOSM: Spam Filter

Author: Andrei Cristian
Version: 0.1

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📌 Overview

SSOSM (Simple Spam Or Sanitized Mail) is a Python-based spam filtering tool designed to classify text or file content using a Naive Bayes classifier trained on manually labeled data. It can detect spam from raw text files and HTML emails using a lightweight and customizable dataset.

This project is ideal for security research, email filtering prototypes, or educational purposes on NLP and machine learning fundamentals.

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📁 Project Structure

.
├── main.py              # Main script containing training and scanning logic
├── requirements.txt     # Dependencies
├── .python-version      # Python version (3.7.12)

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🛠 Features

• ✅ Detects spam content using Multinomial Naive Bayes
• ✅ Supports plain text and HTML content (auto-strips HTML)
• ✅ Trains on a CSV dataset (dataset.csv) generated from labeled folders
• ✅ Scans directories for potential spam files
• ✅ Outputs results to a log file
• ✅ Saves and reuses trained model and vectorizer with pickle

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🧪 Requirements

Python 3.7.12
Install dependencies with:

pip install -r requirements.txt

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⚙️ Usage

1. Print Project Info

python main.py -info info.txt

Writes project metadata to the file info.txt.

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2. Scan a Folder for Spam

python main.py -scan <directory_path> <output_file>

Scans all files in the specified directory, classifies them, and writes results to the output file. Each line contains:

<filename>|cln  # Clean
<filename>|inf  # Infected (Spam)

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3. (Optional) Train Model from Labeled Dataset

Note: Training logic is currently commented out in main.py. You can enable it manually for re-training.

Directory structure for training:

<root>
├── Lot1/
│   ├── Clean/
│   └── Spam/
└── Lot2/
    ├── Clean/
    └── Spam/

Each subfolder should contain text files. After uncommenting the train() function and adjusting the logic:

# Enable and modify in main.py
# python main.py -train <path_to_data_root>

It generates a new dataset.csv, trains the classifier, and saves:

• naive_bayes_clf.pkl – the trained model
• naive_bayes_cv.pkl – the fitted CountVectorizer

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🔒 Security Notes

• Only plain text and HTML are processed (malicious scripts in HTML are stripped).
• Binary files are ignored.
• This tool is not a full antivirus scanner — it is intended for text-based spam detection.

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📄 Output Example

offer.txt|inf
newsletter.html|cln
free-gift.msg|inf

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🧹 Future Improvements

• ✅ Enable training via CLI argument
• 📊 Add confusion matrix and classification reports
• 🤖 Extend to detect phishing keywords
• 🗂 Add support for .eml email formats
• 🌐 Web dashboard (Flask/FastAPI)

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📜 License

This project is currently not licensed. Contact the author for reuse or contributions.