DRLMDS: A Deep Reinforcement Learning-Based Scheduling Algorithm for Mimic Defense Servers
Xiaoyun Liao, Sen Yang, Liangjing OuYang, Rong Wu, Xin Huang, Shengjie Yu, Jinzhou Mao, Shangdong Liu, Yimu Ji · Symmetry · 2025
Mimic defense, as an emerging active defense architecture, enhances the resilience of critical systems against unknown attacks through diversified redundant executors and dynamic switching mechanisms. However, the structural heterogeneity and dynamic behaviors of such systems pose great challenges for efficient and secure task scheduling, which traditional algorithms fail to address effectively. To overcome these limitations, this paper proposes a deep reinforcement learning-based scheduling algorithm for mimic defense servers, termed DRLMDS, which integrates an improved particle swarm optimization strategy to construct an environment-adaptive scheduling model capable of perceiving system state changes and optimizing task-resource allocation among heterogeneous executors. The algorithm is validated on mimic defense server datasets containing multiple heterogeneous nodes, where symmetric resource distribution and adjudication mechanisms are explicitly modeled to ensure balanced load distribution and robustness. Experimental results demonstrate that DRLMDS not only effectively defends against malicious attacks but also achieves approximately 30% reduction in task response time, 25% improvement in resource utilization, and nearly 40% enhancement in system stability compared with traditional swarm intelligence algorithms. These findings confirm the superior efficiency, robustness, and security advantages of the proposed approach in complex edge computing environments. This study provides a novel approach for intelligent and adaptive task scheduling in mimic defense architectures, offering theoretical support for active defense research and practical guidance for secure system deployment.