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Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
Open accessAbstract
Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.
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“detectors that achieve >99% accuracy on a single dataset routinely lose 10-20 percentage points when transferred cross-dataset”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [14]
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Nazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., & Pathan, M. S. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101820 · Cited by 134
Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review
Al-Shurbaji, T., Anbar, M., Manickam, S., Hasbullah, I. H., Alfriehat, N. A., Alabsi, B. A., Alzighaibi, A. R., & Hashim, H. A. (2025) · IEEE Access · doi:10.1109/access.2025.3526711 · Cited by 129
A Review of IoT Firmware Vulnerabilities and Auditing Techniques
Bakhshi, T., Ghita, B., & Kuzminykh, I. (2024) · Sensors · doi:10.3390/s24020708 · Cited by 89
BotStop : Packet-based efficient and explainable IoT botnet detection using machine learning
Alani, M. M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.06.039 · Cited by 79
A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
Vu, S. N. T., Stege, M., El-Habr, P. I., Bang, J., & Dragoni, N. (2021) · Future Internet · doi:10.3390/fi13080198 · Cited by 62
Metadata
- Authors
- Wazzan, M., Algazzawi, D., Bamasaq, O., Albeshri, A., & Cheng, L.
- Affiliations
- King Abdulaziz University (SA)
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wazzan, Majda
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-06-20
- Volume
- 11
- Issue
- 12
- Pages
- 5713
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3175402960
- Cited by
- 102
- References
- 106
- Citations by year
- 2026: 12 · 2025: 27 · 2024: 30 · 2023: 16 · 2022: 12 · 2021: 5
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Abdulaziz University
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- IoT botnets, botnet detection, Internet of Things security, IoT device security, research gaps, future research directions
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