MFGD3QN: Enhancing Edge Intelligence Defense Against DDoS With Mean-Field Games and Dueling Double Deep Q-Network

Distributed Denial-of-Service (DDoS) attacks pose a serious threat to the stability and security of edge intelligence devices (EIDs). To solve this issue, we first describe the cost of edge intelligence environments in detail and introduce the mean-field term, edge intelligence repair speed, and DDoS attack intensity, which provide a theoretical basis for the subsequent model construction. Second, based on Hamilton–Jacobi–Bellman backward and Fokker–Planck–Kolmogorov forward equations, a mean-field model is proposed. The optimal DDoS defense policy is then solved considering the strongest DDoS attack intensity and the best EID repair speed. Further, the focus is turned to the interaction between DDoS attackers and the edge intelligence environment. We give the update rule of the mean-field term and construct a mean-field game (MFG) model with a value function. Finally, we propose a multiagent deep reinforcement learning algorithm called MFGs with dueling double deep
$Q$
-learning network (MFGD3QN) to solve the optimal DDoS defense policy problem under the MFG model. In the experiment, we compare MFGD3QN with several benchmark algorithms and verify the superiority of the MFGD3QN algorithm in an edge intelligence environment. We also carry out experiments on different hyperparameters of the MFGD3QN algorithm, lower visibility conditions of the edge intelligence environment, and different repair speeds of EIDs, which verify the robustness and feasibility of the algorithm.