基于评分卡—随机森林的云计算用户公共安全信誉模型研究

doi:10.11959/j.issn.1000-436x.2018085

Abstract

Abstract:

Traditional cloud computing trust models mainly focused on the calculation of the trust of users’ behavior.In the process of classification and evaluation,there were some problems such as ignorance of content security and lack of trust division verification.Aiming to solve these problems,cloud computing users’ public safety trust model based on scorecard-random forest was proposed.Firstly,the text was processed using Word2Vec in the data preprocessing stage.The convolution neural network (CNN) was used to extract the sentence features for user content tag classification.Then,scorecard method was used to filter the strong correlation index.Meanwhile,in order to establish the users’ public safety trust evaluation model in cloud computing,a random forest method was applied.Experimental results show that the proposed users’ public safety trust evaluation model outperforms the general trust evaluation model.The proposed model can effectively distinguish malicious users from normal users,and it can improve the efficiency of the cloud computing users management.

Key words: cloud computing security, security regulation, scorecard, random forest, convolution neural network

CLC Number:

TP393

Shengli ZHOU,Canghong JIN,Lifa WU,Zheng HONG. Research on cloud computing users’ public safety trust model based on scorecard-random forest[J]. Journal on Communications, 2018, 39(5): 143-152.

Figures/Tables 12

评估指标	计算式	说明
准确率	$a c c u r a c y = \frac{T P + T N}{P + N}$	表示模型在测试集上的准确性，即模型正确分类的样本数所占比例
灵敏度	$s e n s i t i v e = \frac{T P}{P}$	正确判断的正样本的百分比
特异度	$s p e c i f i c i t y = \frac{T N}{N}$	正确判断的负样本的比例
精确率	$p r e c i s i o n = \frac{T P}{T P + F P}$	被标记为正样本的样本实际为正样本的比例
召回率	$r e c a l l = \frac{T P}{T P + F N} = \frac{T P}{P}$	是一个完全性度量，同灵敏度
F1值	$F 1 = \frac{2 p r e c i s i o n \cdot r e c a l l}{p r e c i s i o n + r e c a l l}$

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用户公共安全信誉级别	用户行为危险级别
优	正常
良	低危
中	中危
差	高危

IV值	影响效果
< 0.02	对预测变量几乎无影响，不保留
0.02~0.1	对预测变量有一定影响，可保留
0.1~0.3	对预测变量有较大影响，可保留
0.3~0.5	对预测变量有很大影响，可保留
> 0.5	不现实，不保留

敏感话题	TP	TN	FP	FN	准确率	灵敏度	特异度	精确率	F1
A	97	1 634	3	2	0.997 11	0.979 79	0.998 16	0.970 00	0.974 87
B	93	1 637	2	4	0.996 54	0.958 76	0.998 77	0.978 94	0.968 75
C	96	1 634	4	2	0.996 54	0.979 59	0.997 55	0.960 00	0.969 69
D	71	1 662	2	1	0.998 27	0.986 11	0.998 79	0.972 60	0.979 31
E	96	1 634	4	2	0.996 54	0.979 59	0.997 55	0.960 00	0.969 69
F	78	1 648	2	8	0.994 23	0.906 97	0.998 78	0.975 00	0.939 75
G	96	1 634	4	2	0.996 54	0.979 59	0.997 55	0.960 00	0.969 69
H	97	1 625	13	1	0.991 93	0.989 79	0.992 06	0.881 81	0.932 69
I	93	1 623	9	11	0.988 47	0.894 23	0.994 48	0.911 76	0.902 91
J	87	1 636	3	10	0.992 51	0.896 90	0.998 16	0.966 66	0.930 48
K	88	1 605	30	13	0.975 23	0.871 28	0.981 65	0.745 76	0.803 65
L	83	1 646	3	4	0.995 96	0.954 03	0.998 18	0.965 11	0.959 53
M	90	1 636	4	6	0.994 23	0.937 50	0.997 56	0.957 44	0.947 36
N	78	1 633	2	23	0.985 59	0.772 27	0.998 77	0.975 00	0.861 87
O	95	1 633	5	3	0.995 39	0.969 38	0.996 94	0.950 00	0.959 59
P	105	1 625	3	3	0.996 54	0.972 22	0.998 15	0.972 22	0.972 22
Q	97	1 633	3	3	0.996 54	0.970 00	0.998 16	0.970 00	0.970 00
R	96	1 634	4	2	0.996 54	0.979 59	0.997 55	0.960 00	0.969 69

区间	WOE	总数	正例数	负例数	区间占比	正例占比	负例占比
(?∞,1]	?1.399	111	36	75	14.07%	6.91%	27.99%
(1,3]	?1.054	52	21	31	6.59%	4.03%	11.57%
(3,8]	?0.25	93	56	37	11.79%	10.75%	13.81%
(76,109]	?0.207	80	49	31	10.14%	9.4%	11.57%
(8,12]	0.104	60	41	19	7.6%	7.87%	7.09%
(109,194]	0.207	78	55	23	9.89%	10.56%	8.58%
(43,76]	0.207	78	55	23	9.89%	10.56%	8.58%
(12,26]	0.945	84	70	14	10.65%	13.44%	5.22%
(26,43]	1.445	74	66	8	9.38%	12.67%	2.99%
(194,+∞)	1.666	79	72	7	10.01%	13.82%	2.61%

指标名称	中文解释	类型	分箱方法	IV
bad_total	负面占比	double	等频	0.363
middle_total	中性占比	double	等频	0.446
good_total	正面占比	double	等频	1.454
cnt_bad	负面句子数	bigint	等频	0.363
sum_sentence	句子总数	bigint	等频	0.700
cnt_middle	中性句子数	bigint	等频	0.521
cnt_good	正面句子数	bigint	等频	1.225

Research on cloud computing users’ public safety trust model based on scorecard-random forest

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Figures/Tables 12

References 25

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敏感话题	预测值
敏感话题	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P	Q	R
A	97	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0
B	0	93	0	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0
C	0	0	96	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
D	0	0	0	71	0	0	0	0	0	0	0	0	0	0	1	0	0	0
E	0	0	0	0	96	0	0	0	0	0	0	1	0	0	0	0	1	0
F	0	0	2	1	0	78	0	0	0	0	0	0	0	0	1	0	0	4
G	1	0	0	0	1	0	96	0	0	0	0	0	0	0	0	0	0	0
H	0	0	0	0	0	0	0	97	0	0	0	0	0	0	0	0	1	0
I	0	1	0	1	0	0	0	0	93	0	7	0	0	1	1	0	0	0
J	0	0	0	0	0	0	0	0	9	87	0	1	0	0	0	0	0	0
K	0	1	0	0	0	0	1	9	0	0	88	1	0	0	0	0	1	0
L	0	0	0	0	0	0	1	1	0	0	0	83	0	1	1	0	0	0
M	0	0	1	0	0	1	0	0	0	3	1	0	90	0	0	0	0	0
N	1	0	0	0	0	0	1	0	0	0	20	0	0	78	0	1	0	0
O	0	0	0	0	0	1	0	1	0	0	0	0	1	0	95	0	0	0
P	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	105	0	0
Q	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	97	0
R	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	96

分类值	正确个数/个	错误个数/个	总计/个	正确率	召回率	F1指标
1	1 340	503	1 843	72.7%	47.4%	57.4%
0	14 430	1 490	15 920	90.6%	96.6%	93.5%

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