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AuthorShafiq, Muhammad
AuthorTian, Zhihong
AuthorBashir, Ali Kashif
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-29T14:01:37Z
Publication Date2020-07-01
Publication NameComputers and Security
Identifierhttp://dx.doi.org/10.1016/j.cose.2020.101863
CitationShafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2020). IoT malicious traffic identification using wrapper-based feature selection mechanisms. Computers & Security, 94, 101863.‏
ISSN01674048
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084334827&origin=inward
URIhttp://hdl.handle.net/10576/36769
AbstractMachine 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.
SponsorThis work is supported by the National Key research and Development Plan (Grant no. 2018YFB0803504), the Guangdong Province Key Research and Development Plan (Grant no. 2019B010137004) and the National Natural Science Foundation of China under Grant no. 61871140, and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).
Languageen
PublisherElsevier Ltd
SubjectAttacks
Classification
Cybersecurity
Feature selection
Idntification
Internet of things
Machine learning
TitleIoT malicious traffic identification using wrapper-based feature selection mechanisms
TypeArticle
Volume Number94
dc.accessType Open Access


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