IoT event classification based on network traffic

Batyr Charyyev, Mehmet Hadi Gunes

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    28 Scopus citations

    Abstract

    The Internet of Things (IoT) consists of sensors and actuators that facilitate many aspects of our daily life. Compared to typical computing devices such as laptops and smartphones, these devices have a very limited set of functionalities and states. Researchers have shown that it is possible to infer the device type from its network traffic. In this paper, we show that an external observer that sniffs the network traffic of an IoT device can further classify device events and hence infer user actions by employing machine learning classifiers. We evaluate and compare the performance of ten machine learning algorithms in classifying 128 device events from 39 different devices. We analyze the impact of the user interaction through LAN and WAN as well as controllers such as Alexa voice assistant on the correct classification of device actions. We also inspect whether the region from which the device is impacts the performance of classifiers as researchers have shown that differing privacy restrictions lead to different external communications.

    Original languageEnglish
    Title of host publicationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
    Pages854-859
    Number of pages6
    ISBN (Electronic)9781728186955
    DOIs
    StatePublished - Jul 2020
    Event2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada
    Duration: 6 Jul 20209 Jul 2020

    Publication series

    NameIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

    Conference

    Conference2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
    Country/TerritoryCanada
    CityToronto
    Period6/07/209/07/20

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