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

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 6046757
• Main Author: Zheng, Qinghua
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Back propagation networks; Classification; Cloud computing; Computer Simulation; Decision making; Energy consumption; Errors; Evaluation; Fault diagnosis; Humans; Information Systems; Mathematical optimization; Neural networks; Neural Networks, Computer; Optimization; Particle swarm optimization; Principal components analysis; Risk analysis; Risk assessment; Risk factors; Safety and security measures; Security; Variables
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/6046757

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.