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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Open accessAbstract
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
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“Al-Garadi et al. [32] and Pakalapati et al. [33] provide strategic assessments of using AI/ML for threat detection and automated decision-making within DevSecOps pipelines, signalling the start of a broader movement toward intelligent, data-driven security automation.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
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“Among the potential surveyed applications, ML has been used mainly for intrusion detection, while DL for malware and anomalies detection.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“The review is primarily focused on IoT environments and does not directly address the integration of ML/DL into DevSecOps pipelines, which may limit its relevance for general DevSecOps practices.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
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“As main conclusions, the authors claim the need for diverse datasets within IoT security.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“Several security aspects shall be considered in every IoT system (Al-Garadi et al. 2020):”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“For all of these explanations, IoT systems need to go from simply enabling secure communication between devices to intelligence provided by DL approaches to create robust, all-encompassing security solutions, as noted by the al2020survey.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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Akince, B. (2021) · International Journal of Social Inquiry · doi:10.37093/ijsi.950466 · Cited by 5
Integration of Blockchain and Machine Learning for Microgrids
Li, Z., Xu, J. F., Xie, Y., Jiang, J., Zhu, Y., & Yang, X. (2021) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs52626.2021.9449300 · Cited by 5
Chen, J., Qin, Y., Wang, H., & Xu, C. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-021-1669-1 · Cited by 5
An Optimal Scheme for WSN Based on Compressed Sensing
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Aramini, A., Arazzi, M., Facchinetti, T., Ngankem, L. S. Q. N., & Nocera, A. (2022) · IEEE International Workshop on Factory Communication Systems (WFCS) · doi:10.1109/wfcs53837.2022.9779182 · Cited by 5
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Metadata
- Authors
- Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Temple University (US)
- University of Malaya (MY)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 1646-1685
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2885012953
- Semantic Scholar
- da8a949f9c9f1df3a38f12c2cac97b789c705465
- Cited by
- 1,392
- References
- 294
- Citations by year
- 2026: 170 · 2025: 234 · 2024: 232 · 2023: 237 · 2022: 213 · 2021: 217 · 2020: 75
- FWCI
- 87.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NPRP from the Qatar National Research Fund (8-408-2-172, 12S-0305-190231)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things security, machine learning for security, deep learning for security, IoT security threats, attack surface, IoT vulnerabilities, intrusion detection, anomaly detection, authentication, access control, network security
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