[Retracted] Information System Security Evaluation Algorithm Based on PSO‐BP Neural Network

Abstract

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.

A Network Security Situation Assessment Method Based on Fusion Model

“…With the introduction of particle swarm optimization (PSO), some scholars have studied how to apply PSO to the eld of NSSA [11]. PSO belongs to the swarm intelligence algorithm, which has the characteristics of simple structure and strong robustness and performs well in solving combinatorial optimization problems [12][13][14]. In recent years, the research on the PSO algorithm mainly focuses on the optimization of the PSO algorithm and the comparison with other algorithms.…”

A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

“…However, due to its inherent nature as a gradient-based descent algorithm, the traditional BP-ANN model inevitably leads to some shortcomings such as local optimum and the restrictions of error function derivability. Therefore, by incorporating some global optimizing algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) into the training process of the BP algorithm, researchers have proposed a series of hybrid ANN models such as the GA-ANN model and PSO-ANN model and provided some useful results in the relevant literatures [30][31][32][33][34][35][36]. Li et al [35] used SSA-LSTM and SSA-BP ANN models to predict the maximum pitting corrosion depth of subsea oil pipelines.…”

Prevalence of depressive symptoms and associated factors during the COVID-19 pandemic: A national-based study

“…According to previous studies, BPNN has a better prediction function than multiple logistic regression models in complex model fitting and distribution approximation ( Kulkarni et al, 2021 ; Schmidhuber, 2015 ). BPNNs have been widely used in the fields of clinical medicine, computer science, engineering and so on ( Ning et al, 2021 ; Shao et al, 2021 ; Zheng, 2021 ). However, very few examples of the application of BPNN are available in previous research in the field of mental health ( Fan et al, 2021 ; J. Lv et al, 2022 ).…”