Federated Learning for Dynamic Response to low-rate DDOS Attacks in (SDNs)
Budget: $750 – $1,500 USD
- I am seeking a professional to design and implement a federated learning system for dynamic response to low-rate DDoS attacks in distributed Software-Defined Networks (Federated Learning for Dynamic Response to low-rate DDoS Attacks in SDNs).
- The goal of this system is to create a more secure and resilient network infrastructure by integrating the strengths of federated learning and deep reinforcement learning.
- The primary objective is to effectively mitigate various types of cyberattacks, demonstrating improved performance in attack detection and response time compared to traditional methods.
The system utilizes a Deep Q-Network (DQN) to learn optimal defense strategies against cyber-attacks. The federated learning component enables multiple SDN controllers to collaboratively train the DQN model without sharing raw data, thereby preserving data privacy. This approach aims to enhance the robustness and adaptability of the network's defense mechanisms, allowing for real-time, intelligent responses to cyber threats while maintaining a decentralized structure.
- The goal of this system is to create a more secure and resilient network infrastructure by integrating the strengths of federated learning and deep reinforcement learning.
- The primary objective is to effectively mitigate various types of cyberattacks, demonstrating improved performance in attack detection and response time compared to traditional methods.
The system utilizes a Deep Q-Network (DQN) to learn optimal defense strategies against cyber-attacks. The federated learning component enables multiple SDN controllers to collaboratively train the DQN model without sharing raw data, thereby preserving data privacy. This approach aims to enhance the robustness and adaptability of the network's defense mechanisms, allowing for real-time, intelligent responses to cyber threats while maintaining a decentralized structure.