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.
| Author: | Kevin Wallis, Fabian Schillinger, Christoph ReichORCiDGND, Christian Schindelhauer |
|---|---|
| URL: | https://infonomics-society.org/wp-content/uploads/Protection-Measurements-for-Distributed-Decision-Trees.pdf |
| ISSN: | 2349-7009 |
| Parent Title (German): | International Journal for Information Security Research (IJISR) |
| Document Type: | Article (peer-reviewed) |
| Language: | English |
| Year of Completion: | 2021 |
| Release Date: | 2021/12/13 |
| Volume: | 11.2021 |
| Issue: | 1 |
| First Page: | 962 |
| Last Page: | 971 |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |