Skip to content

Latest commit

 

History

History
60 lines (39 loc) · 1.54 KB

README.md

File metadata and controls

60 lines (39 loc) · 1.54 KB

Network Intrusion and Anomaly Detection via TCP/IP Dump Analysis

Project Overview

This project aims to detect network intrusions by analyzing TCP/IP dump files. The system uses machine learning techniques to classify network traffic as normal or intrusive, helping identify potential security threats in real-time.

Key Features

  • TCP/IP Packet Capture: Uses Scapy to capture and analyze network traffic.
  • Machine Learning Model: Trains a Model to detect anomalies in network traffic.
  • Real-time Sniffing: Continuously monitors live network traffic for intrusions.
  • Web Interface: A simple Tkinter-based GUI to run scripts.

Getting Started

1. Requirements

Install the necessary Python packages:

pip install scapy pandas scikit-learn tkinter

2. Run the Scripts

  • Packet Capture: Run nid.py to start capturing and analyzing network packets.

    python nid.py

  • Detect Anomaly: Use anomaly_detect.py to train the anomaly detection model.

    python anomaly_detect.py

  • GUI Interface: Use gui.py to run scripts from a graphical interface.

    python gui.py

Model Evaluation

After training, the model generates a classification report with metrics like precision, recall, and accuracy. Example output:

Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         8
           1       1.00      1.00      1.00         7