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Protection Measurements for Distributed Decision Trees

  • In Industry 4.0 machine learning approaches are a state-of-the art for predictive maintenance, machine condition monitoring, and others. Distributed decision trees are one of the learning algorithms for such applications. A new approach of node based parallelization for the construction is presented and allows to classify data through a network of nodes. Attacks on the nodes are discussed based on different attack scenarios and attack classifications are presented. A thorough analysis of protection measurements is given, such that classification is not maliciously modified by an attacker. Different countermeasures are proposed and analyzed. A quorum-based system allows for a good balance between computational overhead and robustness of the algorithm.

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Author:Kevin Wallis, Fabian Schillinger, Christoph ReichORCiDGND, Christian Schindelhauer
Parent Title (German):International Journal for Information Security Research (IJISR)
Document Type:Article (peer-reviewed)
Year of Completion:2021
Release Date:2021/12/13
First Page:962
Last Page:971
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International