  Cybersecurity breaches are the common anomalies for distributed
cyber-physical systems (CPS). However, the cyber security breach classification
is still a difficult problem, even using cutting-edge artificial intelligence
(AI) approaches. In this paper, we study the multi-class classification problem
in cyber security for attack detection. A challenging multi-node data-censoring
case is considered. In such a case, data within each data center/node cannot be
shared while the local data is incomplete. Particularly, local nodes contain
only a part of the multiple classes. In order to train a global multi-class
classifier without sharing the raw data across all nodes, the main result of
our study is designing a multi-node multi-class classification ensemble
approach. By gathering the estimated parameters of the binary classifiers and
data densities from each local node, the missing information for each local
node is completed to build the global multi-class classifier. Numerical
experiments are given to validate the effectiveness of the proposed approach
under the multi-node data-censoring case. Under such a case, we even show the
out-performance of the proposed approach over the full-data approach.
