This repository contains a project for detecting types of attacks by analyzing network traffic using machine learning techniques.
This project is a network intrusion detection system that can detect the type of attack by analyzing network traffic. The dataset used in this project is the NSL-KDD dataset. The dataset is divided into training and testing datasets. The training dataset is used to train the model and the testing dataset is used to test the model. The model is trained using the Random Forest algorithm. The model is evaluated using the accuracy score. The model is able to detect the type of attack with an accuracy of 97.36%.
- Attack Detection: Identifies different types of network attacks.
- Confusion Matrix: Visual representation of the performance of the classification algorithm.
- main.ipynb: Jupyter Notebook containing the code for data processing, model training, and evaluation.
- Confusion Matrix.png: Image showing the confusion matrix for model performance evaluation.
- Python 3.x
- Jupyter Notebook
- Required libraries (specified in the notebook)
- Clone the repository:
git clone https://github.com/wizaard07/Intrusion-dectection-system
- Change directory:
cd Intrusion-dectection-system