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D3O-IIoT:深度强化学习驱动的动态欺骗编排,用于工业物联网安全
Usman Wushishi1, Altaf Hussain2, Muhammad Imran Khalid1
1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Scientific reports
|December 21, 2025
概括
工业物联网 (IIoT) 网络安全得到了D3O-IIoT的增强,D3O-IIoT是一种强化学习模型,可以动态调节欺骗技术. 这种自适应防御系统显著改善了攻击缓解,同时在资源有限的工业环境中最大限度地减少了虚假警报.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 事物的工业互联网 (IIoT)
背景情况:
- 工业物联网 (IIoT) 系统面临越来越多的网络威胁,利用资源限制和操作漏洞.
- 现有的入侵检测系统缺乏动态适应不断变化的攻击载体,依赖于静态或被动防御.
研究的目的:
- 介绍D3O-IIoT,这是一个新的强化学习模型,用于IIoT网络安全中的动态欺骗编排.
- 根据实时威胁监控,动态协调诸如 honeypots,移动目标防御,虚假遥测和节点隔离等欺骗技术.
主要方法:
- 制定了防御问题作为马尔科夫决策过程.
- 雇佣了一名决斗深度Q网络代理,以最大限度地实现多目标奖励平衡,减轻攻击,欺骗参与,假阳性控制和资源成本.
- 在三个IIoT数据集 (CIC-IIoT2025,WUSTL-IIoT2021,TON-IoT) 上验证了模型.
主要成果:
- 实现了13.7%的攻击缓解率与0.3%的错误报警率,显著超过基线方法 (293-767%的改善).
- 具有强大的概括能力,在不同的数据集中具有高的保留率 (TON-IoT上的97.7%,WUSTL-IIoT上的77.8%).
- 识别了假阳性控制作为最关键的奖励组件,并通过风险值调整显示了可调性.
结论:
- D3O-IIoT为IIoT网络安全提供了一个动态的,基于学习的欺骗编排,超越了基于固定的规则的防御.
- 该模型有效地平衡了多个实际目标,包括在有限的条件下减轻攻击和资源成本.
- 实时部署是可行的,延迟很低 (2.07ms),有利于对已确认的威胁和用于侦察的 honeypots 的隔离.
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