Network Traffic Monitoring and Classification

This project uses a packet sniffer and a pre-trained BERT model to monitor and classify network traffic. It also includes a web-based dashboard for visualizing the network traffic data.

Features

• Packet sniffer that captures and analyzes network traffic
• AI model that classifies network traffic into ALLOW, ALERT, or BLOCK categories
• Dashboard that displays network traffic data and statistics

Installation

Clone the repository:

git clone https://github.com/Chanveasna-ENG/Network-Intrusion-Detection-and-Monitoring-Tool.git

Install the required packages:

pip install -r requirements.txt

Usage

Run the main script to start the packet sniffer and AI prediction model (make sure to run as root or administrator):

python main.py

Run and Open the dashboard in a web browser:

streamlit run Dashboard_v2.py

Screenshots of the other pages of the dashboard

Libraries Used

• Hugging Face Transformers: For loading the pre-trained BERT model
• Streamlit: For creating the dashboard
• Pandas: For data manipulation
• csv: For reading and writing CSV files
• Matplotlib: For data visualization
• Pre-Trained AI Model: https://huggingface.co/rdpahalavan/bert-network-packet-flow-header-payload
• Packet Sniffer (I watched a tutorial on YouTube and modified it to suit my needs): https://www.youtube.com/playlist?list=PL6gx4Cwl9DGDdduy0IPDDHYnUx66Vc4ed