Cybercrime: Identification and Prediction Using Machine Learning Techniques

Table 2

TP, TN, FN, and FP classification for attribute.

UID	Group		Attribute 1
	Cluster classification based on average	GD = 0	New class SVM classifier: Attribute 1	GD = 0	TP	TN	FP	FN
		CD = 1		CD = 1	0	1	11	10
1	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
2	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
3	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
4	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
5	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
6	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
7	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
8	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
9	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
10	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
11	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
12	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
13	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
14	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
15	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
16	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
17	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
18	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
19	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
20	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
21	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
22	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
23	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
24	Genuine User	0	“Crime User”	1	FALSE	TRUE	FALSE	FALSE
25	Genuine User	0	“Genuine User”	0	TRUE	FALSE	FALSE	FALSE