SDN_DDoS_Simulation: A Reinforcement Learning (RL) Based Approach to Defend DDoS Attacks in a SDN Environment
In this era of dynamic cyber threats, the 'SDN_DDoS_Simulation' project hosted on Github sets a promising example and reinforces the importance of staying one step ahead. This project mainly leverages the possibilities presented by a AI-based approach to defend DDoS attacks within a Software-Defined Networking (SDN) environment.
Project Overview:
Created by user 'santhisenan', the SDN_DDoS_Simulation aims to model a SDN network environment and implement defense mechanisms against Distributed Denial of Service (DDoS) attacks. It chiefly uses reinforcement learning to train a model to detect and prevent DDoS attacks. Addressing an ever-pervading need for better security mechanisms in contemporary networks, this project is of significant relevance to network administrators, cybersecurity enthusiasts, and research scholars in the field of computer networks and cyber defense.
Project Features:
SDN_DDoS_Simulation encapsulates core functionalities that cater to the prevention of DDoS attacks. It primarily conducts DDoS simulations, cavorts reinforcement learning for model training, tests the model not merely on the basis of simulation, but also applies it to real world attacks. These operations, with their potential to recognize and parry the DDoS onslaught, brilliantly serve the project's objective of enhancing SDN security.
Technology Stack:
This project leverages multiple programming languages and technologies. Such as Python, SDN, and DDoS, in conjunction with RL algorithms. Python, for its noted proficiency in handling large datasets and ease of use, becomes the coding language of choice. SDN, enabling centralized network control, is the ideal platform for conducting DDoS attack simulations. RL algorithms, known for their ability to learn and adapt, are used for devising DDoS defense mechanisms.
Project Structure and Architecture:
SDN_DDoS_Simulation is structured in a layered fashion. It consists of a data layer (for DDoS attacks), a control layer (for network control), and the application layer (for implementing RL-based defense mechanisms). These layers collaborate to simulate attacks and model defenses, following a smooth workflow for source code execution.
