Network Security Situation Prediction Method Research Articles

Prediction of Network Security Situation Based on Attention Mechanism and Convolutional Neural Network–Gated Recurrent Unit

Network-security situation prediction is a crucial aspect in the field of network security. It is primarily achieved through monitoring network behavior and identifying potential threats to prevent and respond to network attacks. In order to enhance the accuracy of situation prediction, this paper proposes a method that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU), while also incorporating an attention mechanism. The model can simultaneously handle the spatial and temporal features of network behavior and optimize the weight allocation of features through the attention mechanism. Firstly, the CNN’s powerful feature extraction ability is utilized to extract the spatial features of the network behavior. Secondly, time-series features of network behavior are processed through the GRU layer. Finally, to enhance the model’s performance further, we introduce attention mechanisms, which can dynamically adjust the importance of different features based on the current context information; this enables the model to focus more on critical information for accurate predictions. The experimental results show that the network-security situation prediction method, which combines a CNN and a GRU and introduces an attention mechanism, performs well in terms of the fitting effect and can effectively enhance the accuracy of situation prediction.

A Network Security Situation Prediction Method Based on SSA-GResNeSt

A Network Security Situation Prediction Method Based on SSA-GResNeSt

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

The rapid development of information technology has brought much convenience to human life, but more network threats have also come one after another. Network security situation prediction technology is an effective means to protect against network threats. Currently, the network environment is characterized by high data traffic and complex features, making it difficult to maintain the accuracy of the situation prediction. In this study, a network security situation prediction model based on attention mechanism (AM) improved temporal convolutional network (ATCN) combined with bidirectional long short-term memory (BiDLSTM) network is proposed. The TCN is improved by AM to extract the input temporal features, which has a more stable feature extraction capability compared with the traditional TCN and BiDLSTM, which is more capable of processing temporal data, and is used to perform the situation prediction. Finally, by validating on a real network traffic dataset, the proposed method has better performance on multiple loss functions and has more accurate and stable prediction results than TCN, BiDLSTM, TCN-LSTM, and other time-series prediction methods.

Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm.

Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.

Network security situation prediction method based on strengthened LSTM neural network

As an important part of network security situation awareness, network security situation prediction describes the dynamic changes of security situation over time, and predicts future situation values based on historical situation values. In order to improve the accuracy of network security situation prediction, a long- and short-term memory network security situation prediction model based on the Sigmoid weighted reinforcement mechanism is proposed. Firstly, LSTM neural network is used to mine the temporal correlation of network security situation data. Sigmoid weighted linear element is introduced to deal with the gradient problem in the back propagation, and the input value is multiplied by Sigmoid activation function, so as to strengthen the structure of LSTM neural network and improve the accuracy of prediction.Then, the cuckoo search algorithm was used to optimize the super parameters to improve the training time. Finally, the public data set CICIDS2017 was used to verify the model. The simulation experiment results show that the model has a faster convergence rate and smaller errors.

Research and Application of Hybrid Adaptive Forecasting Method Based on SOA

The current network security situation is becoming more and more severe. In order to improve the accuracy of network security situation prediction, a network security situation prediction method based on crowd search algorithm optimized BP neural network is proposed. This algorithm uses the four characteristics of egoism, altruism, pre-action and uncertain reasoning unique to the crowd search algorithm to determine the search strategy, finds the best fitness individual, obtains the optimal weights and thresholds, and then performs random initialization of the BP neural network The threshold and weight are assigned, and the predicted value is obtained after neural network training. Finally, it is compared with the predicted value obtained by the other two optimization algorithms. Experiments show that the algorithm used in network security situation prediction has higher accuracy, smaller errors, and better stability.