Assessment of Information System Assurance Capability Based on SVM

2012 ◽  
Vol 241-244 ◽  
pp. 275-279
Author(s):  
Deng Hui Wu ◽  
Yu Fu ◽  
Jia Sheng Wang
Keyword(s):  
Neural Network ◽  
Information System ◽  
System Security ◽  
Assessment Methods ◽  
Minimum Point ◽  
Security Assurance ◽  
Information System Security ◽  
Subjective Bias

Aiming at the shortcoming of commonly used assessment methods, this paper introduces SVM to information system security assurance capability assessment, builds a corresponding assess model. The simulation result shows that the method can get high assessment accuracy, and can solve the problem of subjective bias brought by experts and the problems of easily trapped into minimum point and over-fitting of neural network, the method is suitable for information system security assurance capability assessment.

scholarly journals Information System Security Evaluation Algorithm Based on PSO-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinghua Zheng
Keyword(s):  
Neural Network ◽  
Information System ◽  
Bp Neural Network ◽  
System Security ◽  
Bp Algorithm ◽  
Security Evaluation ◽  
Information System Security ◽  
The Core ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the deepening of big data and the development of information technology, the country, enterprises, organizations, and even individuals are more and more dependent on the information system. In recent years, all kinds of network attacks emerge in an endless stream, and the losses are immeasurable. Therefore, the protection of information system security is a problem that needs to be paid attention to in the new situation. The existing BP neural network algorithm is improved as the core algorithm of the security intelligent evaluation of the rating information system. The input nodes are optimized. In the risk factor identification stage, most redundant information is filtered out and the core factors are extracted. In the risk establishment stage, the particle swarm optimization algorithm is used to optimize the initial network parameters of BP neural network algorithm to overcome the dependence of the network on the initial threshold, At the same time, the performance of the improved algorithm is verified by simulation experiments. The experimental results show that compared with the traditional BP algorithm, PSO-BP algorithm has faster convergence speed and higher accuracy in risk value prediction. The error value of PSO-BP evaluation method is almost zero, and there is no error fluctuation in 100 sample tests. The maximum error value is only 0.34 and the average error value is 0.21, which proves that PSO-BP algorithm has excellent performance.

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