Network Anomaly Detection

Overview

Network-Anomaly-Detection is a tool for detecting network threats using a Random Forest (RF) model trained on simulated network traffic. This project helps identify potential security threats by analyzing network behavior and classifying anomalies.

Running the App

Setup Environment

1. Activate Conda Environment

   conda activate networkanomaly

2. Install Dependencies
   Ensure all required dependencies are installed

3. Run the Application
   Start the Streamlit app:

   streamlit run results.py

Dataset Used

The model is trained on the UNSW_NB15_training-set (1).csv, a well-known dataset for network intrusion detection.

Features

• Uses a Random Forest model for network threat detection.
• Analyzes simulated network traffic to identify anomalies.
• Provides an interactive visualization of results via Streamlit.

Future Improvements

• Enhance model performance using deep learning techniques.
• Integrate real-time monitoring for live network anomaly detection.
• Implement additional feature engineering techniques to improve accuracy.