[1]张建辉,徐思捷,曾俊杰,等.突变-服务欺骗协同的移动目标防御方法[J].郑州大学学报(工学版),2026,47(4):108-116,133.[doi:10.13705/j.issn.1671-6833.2026.04.019]
　ZHANG Jianhui,XU Sijie,ZENG Junjie,et al.A Mutation-Service Deception Collaborative Moving Target Defense Method[J].Journal of Zhengzhou University (Engineering Science),2026,47(4):108-116,133.[doi:10.13705/j.issn.1671-6833.2026.04.019]
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突变-服务欺骗协同的移动目标防御方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年4期
页码:
108-116,133
栏目:
出版日期:
2026-07-10

文章信息/Info

Title:
A Mutation-Service Deception Collaborative Moving Target Defense Method
文章编号:
1671-6833(2026)04-0108-09
作者:
张建辉1,2, 徐思捷1, 曾俊杰1, 王瑞民3
1. 郑州大学 网络空间安全学院,河南 郑州 450002;2. 嵩山实验室,河南 郑州 450046;3. 郑州大学 计算机与人工智能学院,河南 郑州 450001
Author(s):
ZHANG Jianhui1,2, XU Sijie1, ZENG Junjie1, WANG Ruimin3
1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China; 2. Songshan Laboratory, Zhengzhou 450046, China; 3. School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
关键词:
数字孪生网络;  移动目标防御;  服务欺骗;  深度强化学习
Keywords:
digital twin network;  moving target defense;  service deception;  deep reinforcement learning
分类号:
TP302. 1TP302. 7
DOI:
10.13705/j.issn.1671-6833.2026.04.019
文献标志码:
A
摘要:
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。
Abstract:
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 mechanism 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. Comparative 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.

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更新日期/Last Update: 2026-09-22