Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 19, Issue 2, Pages 2019-2027Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3210139
Keywords
Artificial intelligence (AI); built-in security; deep reinforcement learning; double deep Q network (DDQN); dynamic heterogeneous redundancy (DHR)
Ask authors/readers for more resources
This article presents an AI-assisted trustworthy architecture based on dynamic heterogeneous redundancy (DHR) and deep reinforcement learning-based intelligent arbitration (DRLIA) algorithm to enhance security for industrial Internet of things (IIoT).
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available