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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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Contributor : Adrien Hemmer Connect in order to contact the contributor
Submitted on : Thursday, June 4, 2020 - 4:00:32 PM
Last modification on : Friday, February 4, 2022 - 3:32:48 AM
Long-term archiving on: : Thursday, December 3, 2020 - 1:34:05 PM


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  • HAL Id : hal-02402986, version 1



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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