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Pregled bibliografske jedinice broj: 1101268

Ensemble machine learning approach for classification of IoT devices in smart home


Cvitić, Ivan; Peraković, Dragan; Periša, Marko; Gupta, Brij
Ensemble machine learning approach for classification of IoT devices in smart home // International Journal of Machine Learning and Cybernetics, 12 (2021), 11; 3179-3202 doi:10.1007/s13042-020-01241-0 (međunarodna recenzija, članak, znanstveni)


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Naslov
Ensemble machine learning approach for classification of IoT devices in smart home

Autori
Cvitić, Ivan ; Peraković, Dragan ; Periša, Marko ; Gupta, Brij

Izvornik
International Journal of Machine Learning and Cybernetics (1868-8071) 12 (2021), 11; 3179-3202

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Boosting ; Cybersecurity ; Supervised learning ; Internet of things ; ML

Sažetak
The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport



POVEZANOST RADA


Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Ivan Cvitić (autor)

Avatar Url Marko Periša (autor)

Avatar Url Dragan Peraković (autor)

Citiraj ovu publikaciju:

Cvitić, Ivan; Peraković, Dragan; Periša, Marko; Gupta, Brij
Ensemble machine learning approach for classification of IoT devices in smart home // International Journal of Machine Learning and Cybernetics, 12 (2021), 11; 3179-3202 doi:10.1007/s13042-020-01241-0 (međunarodna recenzija, članak, znanstveni)
Cvitić, I., Peraković, D., Periša, M. & Gupta, B. (2021) Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 12 (11), 3179-3202 doi:10.1007/s13042-020-01241-0.
@article{article, author = {Cviti\'{c}, Ivan and Perakovi\'{c}, Dragan and Peri\v{s}a, Marko and Gupta, Brij}, year = {2021}, pages = {3179-3202}, DOI = {10.1007/s13042-020-01241-0}, keywords = {Boosting, Cybersecurity, Supervised learning, Internet of things, ML}, journal = {International Journal of Machine Learning and Cybernetics}, doi = {10.1007/s13042-020-01241-0}, volume = {12}, number = {11}, issn = {1868-8071}, title = {Ensemble machine learning approach for classification of IoT devices in smart home}, keyword = {Boosting, Cybersecurity, Supervised learning, Internet of things, ML} }
@article{article, author = {Cviti\'{c}, Ivan and Perakovi\'{c}, Dragan and Peri\v{s}a, Marko and Gupta, Brij}, year = {2021}, pages = {3179-3202}, DOI = {10.1007/s13042-020-01241-0}, keywords = {Boosting, Cybersecurity, Supervised learning, Internet of things, ML}, journal = {International Journal of Machine Learning and Cybernetics}, doi = {10.1007/s13042-020-01241-0}, volume = {12}, number = {11}, issn = {1868-8071}, title = {Ensemble machine learning approach for classification of IoT devices in smart home}, keyword = {Boosting, Cybersecurity, Supervised learning, Internet of things, ML} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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