Attack detection in water distribution systems using machine learning

Table 1 A comparison of the algorithms on BATADAL dataset

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