README.md

metadata

title: NSL-KDD Anomaly Detection
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: streamlit
app_port: 8501
pinned: false
tags:
  - anomaly-detection
  - machine-learning
  - streamlit
  - cybersecurity

🧠 NSL-KDD Anomaly Detection (Isolation Forest vs One-Class SVM vs Logistic Regression)

This Streamlit web app performs network intrusion detection using the NSL-KDD dataset,
comparing both unsupervised and supervised machine learning algorithms.

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🌐 Live Demo

👉 Try it on Hugging Face Spaces:
(Replace this link with your Space URL after deployment)

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🧠 Overview

The app demonstrates anomaly detection for cybersecurity by comparing three models:

Model	Type	Description
Isolation Forest	Unsupervised	Detects anomalies by isolating data points
One-Class SVM	Unsupervised	Learns a decision boundary around normal data
Logistic Regression	Supervised	Classifies known attacks using labeled data

The dataset comes from the NSL-KDD benchmark — an improved version of the KDD Cup '99 dataset.

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⚙️ Workflow

1. Load the NSL-KDD dataset from an online source
2. One-hot encode categorical columns (protocol_type, service, flag)
3. Scale all numeric features with StandardScaler
4. Train and compare all three models
5. Display metrics and confusion matrices in Streamlit

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📊 Example Outputs

• Model Performance Table – Accuracy, Precision, Recall, F1-score
• Confusion Matrices – For all three models
• Visual Insights – Easy comparison between supervised and unsupervised models

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🧾 Dataset

Source: University of New Brunswick – NSL-KDD Dataset

It contains 41 features describing network traffic and a label (normal or attack).

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🧰 Tech Stack

• Python 3.9+
• Streamlit – Web framework
• Scikit-learn – ML algorithms & preprocessing
• Matplotlib / Seaborn – Visualization
• Pandas / NumPy – Data handling

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🏗️ Local Setup (Optional)

# Clone the repo
git clone https://huggingface.co/spaces/your-username/nsl-kdd-anomaly-detection
cd nsl-kdd-anomaly-detection

# Install dependencies
pip install -r requirements.txt

# Run Streamlit app
streamlit run app.py