Paper Info
Title
Deep Reinforcement Learning for Securing Software-Defined Industrial Networks With Distributed Control Plane
Abstract
The development of software-defined industrial networks (SDIN) promotes the programmability and customizability of the industrial networks and is suitable to cope with the challenges brought by new manufacturing modes. For building more scalable and reliable SDIN, a distributed control plane with multicontroller collaboration becomes a promising option. However, as the brain of SDIN, the security of the distributed control plane is rarely considered. In addition to suffering direct attacks, each controller is also subjected to attacks propagated by other controllers because of information sharing or management domain takeover, resulting in the spread of attacks in a wider range than a single controller. Therefore, in this article, we study attacks against SDIN with distributed control plane, demonstrate their propagation across multiple controllers, and analyze their impacts. To the best of our knowledge, we are the first to study the security of SDIN with distributed control plane. In addition, since the existing defense mechanisms are not specifically designed for distributed SDIN and cannot defend it perfectly, we propose an attack mitigation scheme based on deep reinforcement learning to adaptively prevent the spread of attacks. Specifically, the novelty of our scheme lies in its ability of learning from the environment and flexibly adjusting the switch takeover decisions to isolate the attack source, so as to tolerate attacks and enhance the resilience of SDIN.
Year
DOI
Venue
2022
10.1109/TII.2021.3128581
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Deep reinforcement learning (DRL),industrial networks,network security,software-defined networking (SDN)
Journal
18
Issue
ISSN
Citations 
6
1551-3203
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jiadai Wang1723.38
Jiajia Liu200.34
Hongzhi Guo300.34
Bomin Mao426513.95