突变-服务欺骗协同的移动目标防御方法OA
A Mutation-Service Deception Collaborative Moving Target Defense Method
针对数字孪生网络(DTN)中突变类移动目标防御(MTD)策略因离散触发而难以在触发间隔内持续拦截恶意流量,易形成防御空窗的问题,提出一种突变-服务欺骗协同的 MTD 方法(MSD-MTD).在地址突变和服务端口突变基础上,引入服务欺骗机制对突变间隔内的可疑流量进行重定向,以增强持续防护能力;进一步结合基于跨节点流量对齐与特征选择的入侵检测方法感知网络状态,并利用深度 Q 网络(DQN)实现 MTD 策略的自适应选择.在 Mininet-WiFi 平台上,基于 CICIDS-2017、CICIDS-2018 和 UNSW-NB15 数据集开展对比实验,并与两种典型地址突变方法进 行 比 较.结果表明:MSD-MTD 在 3 个数据集上的平均防御成功率分别达到 93.36%、88.20%和95.50%,且往返时延主要分布在 0~2 ms,说明所提方法在提升防御效果的同时对网络服务时延影响较小.
To address the problem that once discretely triggered mutation-based moving target defense(MTD)strategies in digital twin network(DTN)could not continuously intercept malicious traffic during trigger intervals,which might result in protection gaps,a mutation-service deception collaborative MTD method,termed MSD-MTD was proposed.Building upon address and service port mutation,MSD-MTD introduced a service deception mecha-nism to redirect suspicious traffic within mutation intervals,thereby enhancing continuous protection.Moreover,an intrusion detection approach based on cross-node traffic alignment and feature selection was employed to perceive network states,and a deep Q-network(DQN)was used to enable adaptive selection of MTD strategies.Compara-tive experiments were conducted on the Mininet-WiFi platform using the CICIDS-2017,CICIDS-2018,and UNSW-NB15 datasets,with performance benchmarked against two representative address-mutation methods.The results showed that MSD-MTD achieved average defense success rates of 93.36%,88.20%,and 95.50%on the three datasets respectively,while the round-trip time was mainly distributed within 0-2 ms,indicating that the proposed method improved defense effectiveness while imposing only limited impact on network service latency.
张建辉;徐思捷;曾俊杰;王瑞民
郑州大学 网络空间安全学院,河南 郑州 450002||嵩山实验室,河南 郑州 450046郑州大学 网络空间安全学院,河南 郑州 450002郑州大学 网络空间安全学院,河南 郑州 450002郑州大学 计算机与人工智能学院,河南 郑州 450001
信息技术与安全科学
数字孪生网络移动目标防御服务欺骗深度强化学习
digital twin networkmoving target defenseservice deceptiondeep reinforcement learning
《郑州大学学报(工学版)》 2026 (4)
108-116,133,10
国家重点研发计划(2023YFB2906401)嵩山实验室资助项目(221100210900)
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