Current cyberspace is confronted with unprecedented security risks, whereas traditional passive protection techniques are ill-equipped for attacks or defects with unknown features. Dynamic heterogeneous redundancy (DHR), a built-in active defense approach, deploys uncertain, random, dynamic systems to change the asymmetry of attack and defense, where arbitration is one of the key mechanisms. In this article, an AI-assisted trustworthy architecture based on DHR and deep reinforcement learning-based intelligent arbitration (DRLIA) algorithm is presented to enhance security for industrial Internet of things (IIoT). A double deep Q network (DDQN) is introduced, which is capable to distinguish the reliable and credible IIoT message from executors through interaction with the DHR environment. Finally, the DRLIA is implemented to conduct arbitration tasks in an IIoT critical message transmission scenario, where several comparison experiments between DRLIA and other traditional algorithms are designed. The result on the testbed empirically demonstrates the effectiveness of the proposed architecture and the security enhancement.

中文翻译：

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基于动态异构冗余的人工智能辅助工业物联网可信架构


当前网络空间面临前所未有的安全风险，而传统的被动防护技术难以应对未知特征的攻击或缺陷。动态异构冗余（DHR）是一种内置的主动防御方法，通过部署不确定的、随机的、动态的系统来改变攻防的不对称性，其中仲裁是关键机制之一。本文提出了一种基于 DHR 和基于深度强化学习的智能仲裁 (DRLIA) 算法的人工智能辅助可信架构，以增强工业物联网 (IIoT) 的安全性。引入了双深Q网络（DDQN），能够通过与DHR环境的交互来区分来自执行者的可靠可信的IIoT消息。最后，实现了DRLIA，以在IIoT关键消息传输场景中执行仲裁任务，并设计了DRLIA与其他传统算法的对比实验。测试台上的结果凭经验证明了所提出的架构的有效性和安全性增强。