jvmolu/Network-Anomaly-Detection

Explore Network Anomaly Detection Project 📊💻. It achieves an exceptional 99.7% accuracy through a blend of supervised and unsupervised learning, extensive feature selection, and model experimentation. Stunning data visualizations using synthetic network traffic data offer insightful representations of anomalies, enhancing network security.

Network Anomaly Detection Project

Welcome to the Network Anomaly Detection project! 🌐🛡️

Overview

This project leverages advanced supervised and unsupervised learning techniques, coupled with meticulous feature selection and model experimentation, to achieve an outstanding 99.7% accuracy in detecting network anomalies on the dataset: https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection/code.

Key Features

Supervised & Unsupervised Learning: Employing both approaches for comprehensive anomaly detection.

Extensive Model Experimentation: Exploration of various models to identify the most effective ones.

Feature Selection Techniques: Meticulous selection methods to enhance model performance.

Data Visualizations: Beautiful visualizations using synthetic network traffic data for insightful analysis 📊💻.

How to Use

1. Clone the repository to your local machine.

2. Install the necessary dependencies listed in the requirements file.

3. Explore the Jupyter notebooks to dive into the code and visualize the data.

4. Experiment with different models and feature selection techniques to enhance detection accuracy.

Future Enhancements

Integration of real-time data streaming for dynamic anomaly detection and Deployment of the model in production environments for continuous monitoring.

Let's make networks safer together! 🚀🔒