🛡️ Network Intrusion Detection System (NIDS)

A Streamlit-based Network Intrusion Detection System that uses Deep Learning and Machine Learning techniques to detect malicious network traffic.
The system supports real-time single input analysis, batch CSV analysis, data visualization, and on-the-fly classification training.

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🚀 Features

🔹 Single Traffic Analysis

• Predicts whether a network connection is NORMAL or an ATTACK
• Uses a pre-trained Deep Learning model (Keras .h5)
• Probability-based decision with a configurable threshold

🔹 Batch CSV Analysis

• Upload large CSV files for analysis
• View:
  • Data preview
  • Descriptive statistics
  • Filtered subsets
• Interactive plots:
  • Line charts
  • Pie charts

🔹 ML Classification Module

• Train a Decision Tree Classifier
• Automatic:
  • Encoding of categorical features
  • Feature scaling
• Outputs:
  • Accuracy score
  • Classification report
  • Confusion matrix

🔹 Modern Dark UI

• Custom CSS
• Clean, dashboard-style interface
• Optimized for wide screens

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🧠 Tech Stack

• Frontend / UI: Streamlit
• Deep Learning: TensorFlow / Keras
• Machine Learning: Scikit-learn
• Data Handling: Pandas, NumPy
• Visualization: Matplotlib
• Model Artifacts: Pickle

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📂 Project Structure

├── app.py # Main Streamlit application ├── nids_model.h5 # Trained deep learning model ├── scaler.pkl # StandardScaler object ├── encoders.pkl # LabelEncoders for categorical features ├── features.pkl # Ordered feature list ├── requirements.txt # Python dependencies └── README.md # Project documentation

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📊 Dataset

• Trained on the KDD Cup Network Intrusion Dataset
• Supports common network attributes such as:
  • protocol_type
  • service
  • flag
  • src_bytes
  • dst_bytes
  • connection counts

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⚙️ Installation & Setup

1️⃣ Clone the Repository

python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows 3️⃣ Install Dependencies bash pip install -r requirements.txt 4️⃣ Update Model Paths Make sure paths to these files are correct in the code:

nids_model.h5

scaler.pkl

encoders.pkl

features.pkl

▶️ Run the Application bash streamlit run app.py The app will open automatically in your browser.

🧪 How Prediction Works User inputs or uploads network traffic data

Categorical features are encoded

Numeric features are scaled

Deep Learning model predicts attack probability

Output:

NORMAL ✅ or ATTACK 🚨

📈 Threshold Logic python threshold = 0.2 label = "ATTACK" if probability > threshold else "NORMAL" You can adjust the threshold based on security requirements.

🛠️ Future Improvements Support for real-time packet capture

Additional ML models (Random Forest, XGBoost)

Model performance comparison dashboard

Deployment on cloud platforms

👤 Author Avikal Bhatt GitHub: https://github.com/avikal286

LinkedIn: https://www.linkedin.com/in/avikal-bhatt-418902372

📜 License This project is for educational and research purposes.

⭐ If you find this project useful, consider giving it a star on GitHub!