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Reinforcement Learning for IoT Security: A Comprehensive Survey
Open accessAbstract
The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and reinforcement learning (RL) is gaining more popularity for securing IoT systems. RL, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this article, we present an comprehensive survey of different types of cyberattacks against different IoT systems and then we present RL and deep RL-based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the RL for securing CPS systems (i.e., IoT with feedback and control), such as smart grid and smart transportation system. The recent important attacks and countermeasures using RL in IoT are also summarized in the form of tables. With this article, readers can have a more thorough understanding of IoT security attacks and countermeasures using RL, as well as research trends in this area.
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“For example, the surveys in [50,70,76] addressed the use of ML-based trust evaluation in IoV focusing on the trust scheme.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [76]
In the citing paper
“As such, it is challenging to protect all these networks against malware attacks, eavesdropping, DoS attacks, and virus injection [76,130].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [76]
In the citing paper
“Despite its strengths, ML faces a range of challenges such as failure in the detection of attacks, wrongly classified objects, the recognition of driver monitoring patterns, vehicle theft, and compromised functional safety [57,76,171,172].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [76]
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Metadata
- Authors
- Uprety, A., & Rawat, D. B.
- Affiliations
- Howard University (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-06-01
- Volume
- 8
- Issue
- 11
- Pages
- 8693-8706
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3108669277
- Cited by
- 214
- References
- 124
- Citations by year
- 2026: 28 · 2025: 47 · 2024: 42 · 2023: 52 · 2022: 31 · 2021: 14
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. NSF (CNS/SaTC 2039583, CNS 1650831, 1828811)
- DoD Center of Excellence in AI and Machine Learning at Howard University (W911NF-20-2-0277)
- U.S. Army Research Laboratory
- DoE’s National Nuclear Security Administration (DE-NA0003946)
- U.S. Department of Homeland Security (2017-ST-062-000003)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Internet of Things security, reinforcement learning, deep reinforcement learning, attack countermeasures, cyber-physical security, smart grid, smart transportation, IoT vulnerabilities
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