Treant trains accurate decision tree ensembles that are nearly insensitive to evasion attacks by minimizing an evasion-aware loss via robust splitting and attack invariance.
Random forests
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
SVM and Random Forest detect intrusions in Modbus gas pipeline and OPC UA batch processing traffic, with Random Forest performing slightly better after feature selection and missing data handling.
citing papers explorer
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Treant: Training Evasion-Aware Decision Trees
Treant trains accurate decision tree ensembles that are nearly insensitive to evasion attacks by minimizing an evasion-aware loss via robust splitting and attack invariance.
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Anomaly-based Intrusion Detection in Industrial Data with SVM and Random Forests
SVM and Random Forest detect intrusions in Modbus gas pipeline and OPC UA batch processing traffic, with Random Forest performing slightly better after feature selection and missing data handling.