DDOs attack simuation using Machine learning model

This project is about building a client-server network using python that achieves the following:

=========Server============
The server application will perform the following tasks:
--Opens socket to listen for all incoming requests from the network (or from the local machine using a different port)
--Gather the information for each request and store the request information in a separate file.
--Convert the requested information to a suitable format to be used by the DDoS attack detection model.
--Run the DDoS attack detection model and test all incoming requests.
--Store model classification about each request.
--Generate a report about all incoming requests and the model classification for each request.

=========Client============

The client application (Node) will perform the following tasks:
Read the data from the test dataset and regenerate the requests to the server to mimic a communication like the one from the dataset.
Generate and send different messages with bounded random package sized to the server
Simulate DDoS attacks in a random manner.
The client application can be installed on a separate machine or installed as multiple instances on the same machine.


NOTE: THE DETECTION MODEL ALREADY EXISTS YOU WILL NEED TO USE IT ONLY.

TIME FRAME: Before 30/11/2022
BUDGET: 100$-200$