The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
IoT malicious traffic identification using wrapper-based feature selection mechanisms
Open accessAbstract
Machine Learning (ML) plays very significant role in the Internet of Things (IoT) cybersecurity for malicious and intrusion traffic identification. In other words, ML algorithms are widely applied for IoT traffic identification in IoT risk management . However, due to inaccurate feature selection, ML techniques misclassify a number of malicious traffic in smart IoT network for secured smart applications. To address the problem, it is very important to select features set that carry enough information for accurate smart IoT anomaly and intrusion traffic identification. In this paper, we firstly applied bijective soft set for effective feature selection to select effective features, and then we proposed a novel CorrACC feature selection metric approach. Afterward, we designed and developed a new feature selection algorithm named Corracc based on CorrACC, which is based on wrapper technique to filter the features and select effective feature for a particular ML classifier by using ACC metric. For the evaluation our proposed approaches, we used four different ML classifiers on the BoT-IoT dataset. Experimental results obtained by our algorithms are promising and can achieve more than 95% accuracy.
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Cited by
Artificial intelligence for cybersecurity: Literature review and future research directions
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Deep learning-enabled anomaly detection for IoT systems
Abusitta, A., de Carvalho, G. H. S., Wahab, O. A., Halabi, T., Fung, B. C. M., & Mamoori, S. A. (2023) · Internet of Things · doi:10.1016/j.iot.2022.100656 · Cited by 184
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Metadata
- Authors
- Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M.
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 94
- Pages
- 101863
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3022550689
- Cited by
- 266
- References
- 52
- FWCI
- 20.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- National Natural Science Foundation of China
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- Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2020). IoT malicious traffic identification using wrapper-based feature selection mechanisms. Computers & Security. https://doi.org/10.1016/j.cose.2020.101863
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