NetSentry-AI

This project uses artificial intelligence to detect DDoS attacks on a network based on a machine learning model. It utilizes a network traffic dataset to train a classification model that can identify whether traffic is normal or malicious.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Random Forest Classifier

Installation

1. Clone the repository

git clone https://github.com/Rizki033/Python-AI-based-DDoS-Detection.git

2. Install dependencies

Make sure Python is installed, then use pip to install the required libraries:

pip install pandas scikit-learn

3. Run the project

1. Run data.py to generate the Network_data.csv file:

   python data.py

2. Run app.py to train the model and test detection:

   python app.py

How It Works

Data Preprocessing :

• Load data from Network_data.csv
• Encode categorical variables (protocol_type, service, flag)
• Split data into input variables (X) and output labels (y)
• Divide into training and testing sets

Model Training:

• Use RandomForestClassifier to train a model for DDoS attack detection

Model Testing:

• A test_network function is provided to test new data for potential attacks

Detection Example :

results  data : 1:normal
results  data : 2:normal
results  data : 3:attack
results  data : 4:normal