@article{WallisSchillingerReichetal.2021, author = {Wallis, Kevin and Schillinger, Fabian and Reich, Christoph and Schindelhauer, Christian}, title = {Protection Measurements for Distributed Decision Trees}, journal = {International Journal for Information Security Research (IJISR)}, volume = {11.2021}, number = {1}, issn = {2349-7009}, url = {https://infonomics-society.org/wp-content/uploads/Protection-Measurements-for-Distributed-Decision-Trees.pdf}, 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} }