The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Machine learning approaches to IoT security: A systematic literature review
Abstract
Abstract With the continuous expansion and evolution of IoT applications, attacks on those IoT applications continue to grow rapidly. In this systematic literature review (SLR) paper, our goal is to provide a research asset to researchers on recent research trends in IoT security. As the main driver of our SLR paper, we proposed six research questions related to IoT security and machine learning. This extensive literature survey on the most recent publications in IoT security identified a few key research trends that will drive future research in this field. With the rapid growth of large scale IoT attacks, it is important to develop models that can integrate state of the art techniques and technologies from big data and machine learning. Accuracy and efficiency are key quality factors in finding the best algorithms and models to detect IoT attacks in real or near real-time
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Citations
Cites
1 retracted
Aldaej, A. (2019) · IEEE Access · doi:10.1109/access.2019.2893445 · Cited by 31
RetractedA detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Anomaly-based network intrusion detection: Techniques, systems and challenges
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers & Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Internet of Things security: A survey
Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.04.002 · Cited by 1,303
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,288
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning
Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-019-09344-w · Cited by 1,198
Cited by
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
IoT anomaly detection methods and applications: A survey
Chatterjee, A., & Ahmed, B. S. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100568 · Cited by 323
Internet of Things: Evolution, Concerns and Security Challenges
Malhotra, P., Singh, Y., Anand, P., Bangotra, D. K., Singh, P. K., & Hong, W.-C. (2021) · Sensors · doi:10.3390/s21051809 · Cited by 260
Cherbal, S., Zier, A., Hebal, S., Louail, L., & Annane, B. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05616-2 · Cited by 152
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 84
Metadata
- Authors
- Ahmad, R., & Alsmadi, I.
- Venue
- Internet of Things
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 14
- Pages
- 100365
- ISSN
- 2542-6605 (ISSN-L)
- OpenAlex
- W3128470182
- Cited by
- 285
- References
- 108
- FWCI
- 11.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ahmad, R., & Alsmadi, I. (2021). Machine learning approaches to IoT security: A systematic literature review. Internet of Things. https://doi.org/10.1016/j.iot.2021.100365
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
