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Journal article
Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks
Computing&AI Connect, Vol.1(1), p.1
12/31/2025
Abstract
The explosive growth of the Internet of Things (IoT) has significantly increased networked devices within distributed and heterogeneous networks. Due to these networks’ inherent vulnerabilities and diversity, the proliferation of IoT devices presents substantial security challenges. Traditional security solutions face challenges in keeping up with the constantly changing threats in dynamic situations. This article reviews the application of distributed Reinforcement Learning approaches to enhance IoT security in dispersed and heterogeneous networks. This paper provides a comprehensive overview of the fundamental theories reinforcing IoT security. It also explores the basis of Distributed Reinforcement Learning and discuss its benefits and drawbacks for IoT security. Then, the focus is given on how Distributed Reinforcement Learning might address these issues and offer details on the design factors to consider when implementing Distributed Reinforcement Learning-based solutions into practice. The paper outlines case studies and experiments that show how Distributed Reinforcement Learning may enhance IoT security. It also addresses performance analysis and evaluation measures to compare Distributed Reinforcement Learning-based approaches with conventional security methods. Finally, the paper highlights the possible uses of Distributed Reinforcement Learning in IoT security and suggest future directions, emerging trends, and unresolved challenges.
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Details
- Title
- Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks
- Publication Details
- Computing&AI Connect, Vol.1(1), p.1
- Resource Type
- Journal article
- Publisher
- Scifiniti Publishing
- Identifiers
- 99381851902006600
- Academic Unit
- Cybersecurity and Information Technology; Hal Marcus College of Science and Engineering
- Language
- English