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Table 4 A comparison of the algorithms BATADAL dataset with multi-stage implementation

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 0.9503 0.9584 0.9422 0.8945 0.9165 0.9679 0.8735 373 54 1628 34
3 B2 7 0.9491 0.9580 0.9402 0.8813 0.9214 0.9590 0.8446 375 69 1613 32
4 Ensemble 7 0.9464 0.9400 0.9529 0.9061 0.9361 0.9696 0.8779 381 53 1692 26
5 MD 7 0.9342 0.9297 0.9387 0.9006 0.9017 0.9756 0.8995 367 41 1641 40
6 B3 7 0.9267 0.9360 0.9174 0.9057 0.8378 0.9970 0.9855 341 5 1677 66
7 LOF 7 0.9258 0.9229 0.9286 0.8875 0.8821 0.9752 0.8930 359 43 1693 48
8 SOD 7 0.9157 0.9091 0.9223 0.8462 0.8993 0.9453 0.7991 366 92 1590 41
9 B4 6 0.8942 0.8570 0.9313 0.8894 0.8894 0.9732 0.8894 362 45 1637 45
10 B5 7 0.8015 0.8350 0.7679 0.5382 0.8575 0.6784 0.3921 349 541 1141 58
11 LDA 6 0.7745 0.7959 0.7532 0.6677 0.5111 0.9952 0.9630 208 8 1674 199
12 B6 7 0.7727 0.8850 0.6605 0.4829 0.3292 0.9917 0.9054 134 14 1668 273
13 OSVM 7 0.7721 0.7383 0.8060 0.7538 0.6167 0.9952 0.9691 251 8 1674 156
14 Naïve 7 0.7500 1.0000 0.5000 0.3261 1.0000 0.0000 0.1948 407 1682 0 0
15 B7 3 0.5344 0.4290 0.6398 0.4220 0.3956 0.8841 0.4522 161 195 1487 246