Cybersecurity　breaches　are　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　article,　we　study　a　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,　we　design　a　multi-node　multi-class　classification　ensemble　approach　which　is　the　main　result　of　our　study.　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　datacensoring　case.　Under　such　a　case,　we　even　show　the　out-performance　of　the　proposed　approach　over　the　full-data　approach.