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A Process Mining Approach for Supporting IoT Predictive Security

Abstract : The growing interest for the Internet-of-Things (IoT) is supported by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex and value-added systems and applications. While important efforts have been done for their protection, security management is a major challenge for these systems, due to their complexity, their heterogeneity and the limited resources of their devices. In this paper we introduce a process mining approach for detecting misbehaviors in such systems. It permits to characterize the behavioral models of IoT-based systems and to detect potential attacks, even in the case of heterogenous protocols and platforms. We then describe and formalize its underlying architecture and components, and detail a proof-of-concept prototype. Finally, we evaluate the performance of this solution through extensive experiments based on real industrial datasets.
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https://hal.inria.fr/hal-02402986
Contributor : Adrien Hemmer <>
Submitted on : Thursday, June 4, 2020 - 4:00:32 PM
Last modification on : Thursday, June 4, 2020 - 4:10:33 PM
Long-term archiving on: : Thursday, December 3, 2020 - 1:34:05 PM

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Adrien Hemmer, Remi Badonnel, Isabelle Chrisment. A Process Mining Approach for Supporting IoT Predictive Security. NOMS 2020 - IEEE/IFIP Network Operations and Management Symposium, Apr 2020, Budapest, Hungary. ⟨hal-02402986⟩

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