The DOI matches its Crossref record; 104 of the 106 references it cites were found in a registry or an index.
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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Cites
1 retracted
Mubarakali, A., Srinivasan, K., Mukhalid, R., Jaganathan, S. C. B., & Marina, N. (2020) · Computational Intelligence · doi:10.1111/coin.12293 · Cited by 36
RetractedPerforming systematic literature reviews in software engineering
Budgen, D., & Brereton, P. (2006) · International Conference on Software Engineering · doi:10.1145/1134285.1134500 · Cited by 8,824
Systematic Reviews in the Social Sciences: A Practical Guide
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N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018) · IEEE Pervasive Computing · doi:10.1109/mprv.2018.03367731 · Cited by 1,668
A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures
Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., & Sikdar, B. (2019) · IEEE Access · doi:10.1109/access.2019.2924045 · Cited by 1,445
A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
Sengupta, J., Ruj, S., & Das Bit, S. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102481 · Cited by 844
Flagged first, then by citations
2 of the 106 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- Alhajri, 2019, Survey for anomaly detection of IoT botnets using machine learning auto-encoders, Int. J. Appl. Eng. Res., 14, 2417
- De Donno, M., Dragoni, N., Giaretta, A., and Spognardi, A. (2017, January 3–6). Analysis of DDoS-capable IoT malwares. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic.
- Manos, A., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., and Durumeric, Z. (2017, January 16–18). Un-derstanding the mirai botnet. Proceedings of the 26th {USENIX} security symposium ({USENIX} Security 17), Vancouver, BC, Canada.
- Costin, A., and Zaddach, J. (2018, January 3–6). Iot malware: Comprehensive survey, analysis framework and case studies. Proceedings of the BlackHat, Las Vegas, NV, USA.
- Edwards, 2016, Hajime: Analysis of a decentralized internet worm for IoT devices, Rapidity Netw., 16, 1
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- Beek, C. (2021, March 05). McAfee Labs Covide-19 Threat Report. Available online: https://www.mcafee.com/enterprise/en-us/assets/reports/rp-quarterly-threats-july-2020.pdf.
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Items 101–102 of 102
Afzal, S., Gebrehiwot, A., Lauria, F. M., & Khan, F. A. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102109
AI-based early detection of botnet behavior in Medical IOT networks
Alamri, R., & Ibraheem, A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112857
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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