@article{WallisSchillingerReichetal.2021,
  author    = {Kevin Wallis and Fabian Schillinger and Christoph Reich and Christian Schindelhauer},
  title     = {Protection Measurements for Distributed Decision Trees},
  series   = {International Journal for Information Security Research (IJISR)},
  volume    = {11.2021},
  number    = {1},
  issn      = {2349-7009},
  pages     = {962 -- 971},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}