The term “the Industrial Internet of Things” has become increasingly more pervasive in the context of manufacturing as digitization has become a business priority for many manufacturers. IIoT refers to a network of interconnected industrial devices, resulting in systems that can monitor, collect, exchange, analyze, and deliver valuable data and new insights. These insights can then help drive smarter, and faster business decisions for manufacturers. However, these benefits have come at the cost of creating a new attack vector for the malicious agents that aim at stealing manufacturing trade secrets, blueprints, or designs. As a result, cybersecurity concerns have become more relevant across the field of manufacturing. One of the main tracks of research in this field deals with developing effective cyber-security mechanisms and frameworks that can identify, classify, and detect malicious attacks in industrial IoT devices. In this paper, we have developed and implemented a classification and detection framework for addressing cyber-security concerns in industrial IoT which takes advantage of various machine learning algorithms. The results prove the satisfactory performance and robustness of the approach in classifying and detecting the attacks.


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    Titel :

    Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Kim, Kyoung-Yun (Herausgeber:in) / Monplaisir, Leslie (Herausgeber:in) / Rickli, Jeremy (Herausgeber:in) / Shahin, Mohammad (Autor:in) / Chen, F (Autor:in) / Bouzary, Hamed (Autor:in) / Hosseinzadeh, Ali (Autor:in) / Rashidifar, Rasoul (Autor:in)

    Kongress:

    International Conference on Flexible Automation and Intelligent Manufacturing ; 2022 ; Detroit, MI, USA June 19, 2022 - June 23, 2022



    Erscheinungsdatum :

    2022-10-13


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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