Information security risk prediction model based on IIWPSO-BP from perspective of alliance chain

doi:10.16265/j.cnki.issn1003-3033.2022.08.2483

Abstract

Abstract:

In order to find potential information security risks of smart cities in time, an information security risk prediction model was built based on IIWPSO algorithm optimized BP (IIWPSO-BP) neural network algorithm. Firstly, the information security risk index system was constructed by considering six aspects: information owner, shared information, alliance chain technology, information user, alliance chain management and security measures. Secondly, the information security risk prediction model was trained and tested by quantifying the information security risk index. Finally, the robustness, accuracy and time complexity of the model were compared and analyzed. The results show that the mean absolute error (MAE) of the IIWPSO-BP prediction model is 0.137 4, the mean relative error (MRE) is 0.038 5, and the fitting degree is 0.972 0. The prediction accuracy is improved by 37.6% and 65.2%, respectively, compared with the PSO-BP neural network and the BP neural network.

Key words: managementalliance chain, information security, improved inertia weight change mode particle swarm optimization(IIWPSO), back propagation (BP) neural network, risk prediction, smart city

ZHOU Xinmin, LUO Wenmin, LIU Junjie, XIE Bao. Information security risk prediction model based on IIWPSO-BP from perspective of alliance chain[J]. China Safety Science Journal, 2022, 32(8): 52-60.

Figures/Tables 8

Fig.1

Tab.1

Comparison of experimental results that meet the error threshold requirements

试验编号	隐藏层神经元数	最大迭代次数	预测值					MAE	MRE
1	6	1 000	3.912 2	3.817 9	2.403 7	1.757 3	5.110 2	0.243 8	0.081 9
2		1 100	3.703 9	4.604 8	1.864 1	1.929 8	4.594 7	0.502 5	0.144
3		2 000	3.372 9	4.538 7	2.535 5	2.303 3	5.235 7	0.433 9	0.129
4		2 100	3.407 5	3.427 2	2.171	2.129 3	5.081 7	0.441 1	0.129 7
5	7	800	3.804 3	3.794 4	3.340 6	2.030 4	4.772 9	0.199 9	0.054 9
6		2 600	2.806 9	3.551 5	2.558 2	1.819 1	5.355 7	0.524 0	0.143 8
7		3 100	3.109 3	3.480 5	2.281	1.750 6	4.882 8	0.499 2	0.148 1
8	8	300	3.680 6	4.172 1	1.993 4	2.132 7	4.154 1	0.495 3	0.138 8
9		1 000	3.619	3.84	2.634 7	1.631 3	5.077 6	0.270 5	0.091 4
10		1 700	3.161 9	4.256 9	1.951 1	2.079 2	5.138 8	0.472 4	0.138 1
11		2 000	3.851 9	3.747 3	1.821 3	2.296 1	5.180 5	0.411 2	0.135 4
12	9	200	2.634 1	3.373 2	2.756 3	1.96 3	5.108 4	0.476 4	0.123 9
13		600	4.15	4.194 2	3.233 8	2.374 1	5.445 8	0.279 6	0.088
14		1 200	3.672 1	4.047 6	2.852	1.827 5	3.558 8	0.427 4	0.103 6
15		1 600	3.783 9	3.929 1	2.949 6	2.036 1	6.064 4	0.287 6	0.063 9
16		2 000	3.649 2	4.022 3	1.998 5	2.057 5	4.485	0.389 4	0.111 8
17		2 700	3.119 6	4.264 9	2.182 7	1.819 5	4.855 8	0.457 5	0.135 6
18		2 800	2.725 1	3.862 1	3.074	1.786 1	5.182 2	0.376 6	0.104 3
19		5 000	3.308 5	4.081 3	2.543 4	1.822 7	4.959 9	0.289 4	0.088 4
20	10	200	4.266 9	4.406 9	2.235 2	2.311 2	4.807 3	0.388 5	0.123 5
21	10	1 200	3.95 4	4.836 1	1.713 9	2.003 9	5.349 2	0.504 3	0.144 2
22	11	600	2.161 6	4.036 5	2.966 2	1.965 2	6.019 3	0.592 6	0.140 3
23		1 100	4.116 6	3.724 4	2.766 1	1.98 5	4.953 9	0.137 4	0.038 6
24		1 400	3.822 6	3.393 5	2.551 8	1.404 6	5.117 3	0.389 0	0.133 3
25	12	1 000	3.646 1	4.387 1	2.867 8	2.670 3	5.479 5	0.404 6	0.132 1
26	12	1 200	3.523 1	4.033 0	2.489 9	1.683 0	5.339 2	0.335 2	0.104 8
27	13	100	4.370 1	4.442 1	2.225 5	1.547 1	4.791 0	0.449 7	0.145 9
28		400	3.846 5	4.269 4	2.244 6	2.697 8	4.960 0	0.383 2	0.142 9
29		500	3.503 1	4.185 6	2.647	2.665 8	5.303 1	0.400 9	0.136 4
30		1 100	3.407 8	3.174 5	2.784 3	1.533 4	5.265 3	0.473 1	0.142 5
31		1 400	4.006 3	3.820 6	2.669	2.307 5	5.237 3	0.212 3	0.071 6
32		2 500	3.118	4.082 1	1.974 8	2.045 1	5.061 1	0.419 1	0.123 5
33	14	100	3.862 7	5.244 1	3.513 7	1.989 6	6.010 5	0.583 2	0.114 8
34	14	3 000	3.81	3.861 5	2.475 1	1.819 8	5.735 1	0.353 7	0.098 8
35	15	700	4.12	3.147 5	3.356 3	1.685 2	4.178 3	0.493 1	0.136 7

Tab.1

Fig.2

Fig.3

Tab.2

Tab.3

Tab.4

Fig.4

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样本数	IIWPSO-BP预测模型			PSO-BP预测模型			BP神经网络预测模型
	隐藏层神经元数=11 最大迭代次数=1 100			隐藏层神经元数=9 最大迭代次数=1 600			隐藏层神经元数=9 最大迭代次数=5 000
	预测值	AE	RE	预测值	AE	RE	预测值	AE	RE
1	4.116 6	0.116 6	0.029 1	3.617 9	0.382 1	-0.095 5	3.795 5	0.204 5	-0.051 1
2	3.724 4	0.275 6	-0.068 9	3.759 0	0.241 0	-0.060 2	4.005 4	0.005 4	0.001 3
3	2.766 1	0.233 9	-0.078 0	2.770 7	0.229 3	-0.076 4	3.000 3	0.000 3	0.000 1
4	1.985 0	0.015 0	-0.007 5	2.122 9	0.122 9	0.061 5	2.999 8	0.999 8	0.499 9
5	4.953 9	0.046 1	-0.009 2	5.073 3	0.073 3	0.014 7	5.000 8	0.000 8	0.000 2

隐藏层神经元数=11 最大迭代次数=1 100 IIWPSO-BP预测模型的最佳参数)
样本数	IIWPSO-BP预测模型			PSO-BP预测模型			BP神经网络预测模型
样本数	预测值	AE	RE	预测值	AE	RE	预测值	AE	RE
1	4.116 6	0.116 6	0.029 1	4.005 3	0.005 3	0.001 3	3.137 4	0.862 6	-0.215 6
2	3.724 4	0.275 6	-0.068 9	3.146 1	0.853 9	-0.213 5	3.601 4	0.398 6	-0.099 6
3	2.766 1	0.233 9	-0.078 0	2.859 0	0.141 0	-0.047 0	3.025 2	0.025 2	0.008 4
4	1.985 0	0.015 0	-0.007 5	1.643 2	0.356 8	-0.178 4	2.991 9	0.991 9	0.496 0
5	4.953 9	0.046 1	-0.009 2	4.538 5	0.461 5	-0.092 3	4.765 3	0.234 7	-0.046 9
隐藏层神经元数=9 最大迭代次数=1600 (PSO-BP预测模型的最佳参数)
样本数	IIWPSO-BP预测模型			PSO-BP预测模型			BP神经网络预测模型
样本数	预测值	AE	RE	预测值	AE	RE	预测值	AE	RE
1	3.783 9	0.216 1	-0.054	3.617 9	0.382 1	-0.095 5	3.317 9	0.682 1	-0.170 5
2	3.929 1	0.070 9	-0.017 7	3.759	0.241	-0.060 2	3.525 3	0.474 7	-0.118 7
3	2.949 6	0.050 4	-0.016 8	2.770 7	0.229 3	-0.076 4	3.223 2	0.223 2	0.074 4
4	2.036 1	0.036 1	0.018 1	2.122 9	0.122 9	0.061 5	3.232 0	1.232 0	0.616 0
5	6.064 4	1.064 4	0.212 9	5.073 3	0.073 3	0.014 7	4.517 8	0.482 2	-0.096 4
隐藏层神经元数=9 最大迭代次数=5000 (BP神经网络预测模型的最佳参数)
样本数	IIWPSO-BP预测模型			PSO-BP预测模型			BP神经网络预测模型
样本数	预测值	AE	RE	预测值	AE	RE	预测值	AE	RE
1	3.308 5	0.691 5	-0.172 9	2.961 7	1.038 3	-0.259 6	3.795 5	0.204 5	-0.051 1
2	4.081 3	0.081 3	0.020 3	3.854 3	0.145 7	-0.036 4	4.005 4	0.005 4	0.001 3
3	2.543 4	0.456 6	-0.152 2	2.667 9	0.332 1	-0.110 7	3.000 3	0.000 3	0.000 1
4	1.822 7	0.177 3	-0.088 6	1.950 7	0.049 3	-0.024 7	2.999 8	0.999 8	0.499 9
5	4.959 9	0.040 1	-0.008 0	4.793 8	0.206 2	-0.041 2	5.000 8	0.000 8	0.000 2

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