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Table 3 A comparison of the algorithms on noisy 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 0.9441 0.9584 0.9298 0.8870 0.8870 0.9727 0.8870 361 46 1636 46
2 MD 7 0.9010 0.9024 0.8995 0.8719 0.8108 0.9881 0.9429 330 20 1662 77
3 Ensemble 7 0.8609 0.9040 0.8178 0.6802 0.7420 0.8936 0.6279 302 179 1503 105
4 LOF 7 0.8507 0.8814 0.8201 0.7208 0.6978 0.9423 0.7454 284 97 1585 123
5 SOD 6 0.8267 0.8172 0.8362 0.6660 0.8354 0.8371 0.5537 340 274 1408 67
6 Naive 7 0.7500 1.0000 0.5000 0.3261 1.0000 0.0000 0.1948 407 1682 0 0
7 OSVM 6 0.6420 0.6449 0.6392 0.4351 0.2801 0.9982 0.9744 114 3 1679 293
8 LDA 4 0.5117 0.4444 0.5790 0.2737 0.1597 0.9982 0.9559 65 3 1679 342