Abstract
The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents.
Highlights
We present a novel MARL system implemented with MADDPG algorithm for traffic burst, elephant flow, and DDoS detection and prevention in an software-defined networks (SDNs)-IoT environment
The reward is used to measure the results of the obtained action to a respective corresponding state that is perceived from the environment
Average global reward is computed for 3000 episodes
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. RL agents have seen dynamic in operators and the possible malicious intrusion from compromised IoT devices by attackers. The network must be protected attack loopholes [19] in th Reinforcement Learning, has seen breakthroughs in complex game development, robotics, tecture while engineering dynamic multi‐path routing [20,21] strategies for and network automation, among others, by using hidden layers to capture features and benign. The security of DL models and data privacy protection has been studied botics, and network automation, among others, by using hidden layers to cap in [23,24] and remains a concern to service providers, especially with the massive connectivand details relevant tolead thetoRL agentsmodels [2,22].
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