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Comparative Assessment of Process Mining for Supporting IoT Predictive Security

Abstract : The growth of the Internet-of-Things (IoT) has been characterized by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex systems and applications. 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 evaluate the exploitability and performance of a process mining approach for detecting misbehaviors in such systems. We describe the considered architecture and detail its operation, from the generation of behavioral models to the detection of potential attacks. We formalize several alternative commonly-used detection methods, including elliptic envelope, support-vector machine, local outlier factor, and isolation forest techniques. After presenting a proofof-concept prototype, we quantify comparatively the benefits and limits of our process mining solution combined with data preprocessing, through extensive experiments based on different industrial datasets.
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Contributor : Adrien Hemmer Connect in order to contact the contributor
Submitted on : Monday, November 23, 2020 - 3:42:14 PM
Last modification on : Wednesday, November 3, 2021 - 7:08:57 AM
Long-term archiving on: : Thursday, February 25, 2021 - 1:34:56 PM


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Adrien Hemmer, Mohamed Abderrahim, Rémi Badonnel, Jérôme François, Isabelle Chrisment. Comparative Assessment of Process Mining for Supporting IoT Predictive Security. IEEE Transactions on Network and Service Management, IEEE, In press, ⟨10.1109/TNSM.2020.3038172⟩. ⟨hal-03019862⟩



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