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Table 1 A comparison of the algorithms on 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 B1 7 0.9701 0.9650 0.9752 0.9700 0.9533 0.9970 0.9873 388 5 1677 19
2 QDA 7 0.9495 0.9584 0.9406 0.8981 0.9091 0.9721 0.8873 370 47 1635 37
3 B2 7 0.9491 0.9580 0.9402 0.8813 0.9214 0.9590 0.8446 375 69 1613 32
4 B3 7 0.9267 0.9360 0.9174 0.9057 0.8378 0.9970 0.9855 341 5 1677 66
5 MD 7 0.9165 0.9069 0.9260 0.8920 0.8722 0.9798 0.9126 355 34 1648 52
6 Ensemble 7 0.9142 0.8998 0.9286 0.8856 0.8845 0.9727 0.8867 360 46 1636 47
7 B4 6 0.8942 0.8570 0.9313 0.8894 0.8894 0.9732 0.8894 362 45 1637 45
8 LOF 7 0.8773 0.8567 0.8978 0.8560 0.8182 0.9774 0.8976 333 38 1644 74
9 SOD 7 0.8617 0.8350 0.8884 0.8120 0.8280 0.9489 0.7967 337 86 1596 70
10 B5 7 0.8015 0.8350 0.7679 0.5382 0.8575 0.6784 0.3921 349 541 1141 58
11 B6 7 0.7727 0.8850 0.6605 0.4829 0.3292 0.9917 0.9054 134 14 1668 273
12 Naive 7 0.7500 1.0000 0.5000 0.3261 1.0000 0.0000 0.1948 407 1682 0 0
13 OSVM 7 0.7143 0.6967 0.7319 0.6332 0.4644 0.9994 0.9947 189 1 1681 218
14 LDA 5 0.6787 0.6575 0.6999 0.5709 0.4005 0.9994 0.9939 163 1 1681 244
15 B7 3 0.5344 0.4290 0.6398 0.4220 0.3956 0.8841 0.4522 161 195 1487 246