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Table 2 A comparison of the algorithms on reduced BATADAL dataset

From: Attack detection in water distribution systems using machine learning

Rank Name No. attacks S \(S_{TTD}\) \(S_{CLF}\) \(F_1\) TPR TNR PPV TP FP TN FN
1 QDA 7 0.9412 0.9448 0.9376 0.9032 0.8966 0.9786 0.9100 182 18 824 21
2 MD 7 0.8797 0.8536 0.9059 0.8877 0.8177 0.9941 0.9708 166 5 837 37
3 LOF 6 0.8553 0.8188 0.8918 0.7843 0.8696 0.9141 0.7143 180 72 766 27
4 Ensemble 6 0.8469 0.8245 0.8692 0.7211 0.8744 0.8640 0.6136 181 114 724 26
5 SOD 6 0.8284 0.8053 0.8515 0.6836 0.8621 0.8409 0.5663 175 134 708 28
6 OSVM 6 0.7824 0.7814 0.7833 0.6385 0.6700 0.8967 0.6099 136 87 755 67
7 Naive 7 0.7500 1.0000 0.5000 0.3261 1.0000 0.0000 0.1948 407 1682 0 0
8 LDA 4 0.6390 0.5588 0.7192 0.6096 0.4384 1.0000 1.0000 89 0 842 114