Network Security Situation Prediction 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.

VTion-PatchTST: Elevated PatchTST model for network security situation prediction

The rapid development of network attack techniques increases the risk of the internet being attacked, thus necessitating timely and effective network protection mechanisms. Network security situation prediction is a proactive defense method, the results of which can help formulate defense strategies in advance. The current network security situation prediction mainly focuses on single-step prediction, and the accuracy of multi-step prediction needs to be improved. By introducing three technologies: Variational Mode Decomposition (VMD), Temporal Convolutional Networks (TCN), Feature Fusion, an improved PatchTST model is proposed to solute the issue. VMD decomposes a complex sequence of historical situational values into multiple relatively stable subsequences with different features to make the model channel modeling specific. TCN enhances the ability of the model to extract temporal features. Feature fusion makes predictions more comprehensive through cross-channel linking. Both experiments and evaluations are conducted on the UNSW-NB15 and CIC-IDS2017 public datasets, and nine baseline models are used to compare with the proposed VTion-PatchTST. The experimental results show that the proposed model is more applicable to cybersecurity situation forecasting, with an relative average reduction of 40.3% and 29.0% in Mean Square Error (MSE) and Mean Absolute Error (MAE), respectively.

PatchesNet: PatchTST-based multi-scale network security situation prediction

Internet of Things (IoT) technology increases connectivity between devices; however, IoT device networks are less secure, making them highly susceptible to attack. Most of the network security defense techniques are passive defense with inevitable delays. For instance, in the case of intrusion detection systems, during the time interval between detecting an attack and implementing defenses, the attacker may inflict damage on the target. Therefore, the adoption of active defense techniques becomes particularly important. Network Security Situation Prediction (NSSP) is an active defense technology capable of predicting future states of network security. Currently, most methods in the NSSP field focus on single-step prediction, while research related to multi-step prediction is scarce. Hence, this paper presents an NSSP method based on the PatchTST framework(PatchesNet), which exhibits good multi-step predictive performance. The method uses Variational Mode Decomposition (VMD) to deal with sequence instability. Multi-scale Patch delivery (MSP), Multi-scale Second Decomposition (MSD), and Period Embedding (PeE) are used to obtain more sequence features. Correlation Fusion (CF) can reduce the noise effect on prediction. Experiments are conducted on three datasets. The results demonstrate that PatchesNet exhibits favorable performance, relatively reducing the MSE and MAE by 39.243% and 26.033%, respectively, compared to the previous state-of-the-art method.

A Network Security Situation Prediction Method Based on SSA-GResNeSt

A Network Security Situation Prediction Method Based on SSA-GResNeSt

Construction and Analysis of QPSO-LSTM Model in Network Security Situation Prediction

The continuous improvement of artificial intelligence technology has deepened its application in many fields and provided more support for predicting network security situations. QPSO-LSTM model based on LSTM neural network and fused with QPSO algorithm provides more options for improving network security situation prediction, further enhancing the effectiveness of network security situation prediction, and enabling more efficient and accurate prediction and analysis of network security situations. By comparing the applications of different types of algorithms in network security situation prediction, it was found that the QPSO-LSTM model has smaller prediction errors, can achieve higher prediction accuracy, and can also obtain higher F1-score and AUC values; the shorter identifying runtime also lays the foundation for improving the speed and efficiency of network security situation prediction. Therefore, in the field of network security situation prediction, the application of QPSO-LSTM model can provide more support for further improvement and improvement of security situation prediction performance in this field.

A Network Security Situation Prediction for Consumer Data in the Internet of Things Using Variational Mode Decomposition (VMD) and Fused CNN-BiLSTM-Attention

A Network Security Situation Prediction for Consumer Data in the Internet of Things Using Variational Mode Decomposition (VMD) and Fused CNN-BiLSTM-Attention

Network Security Situation Prediction Technology Based on Fusion of Knowledge Graph

Network Security Situation Prediction Technology Based on Fusion of Knowledge Graph

ALSNAP: Attention-based long and short-period network security situation prediction

The Internet of Things (IoT) and other emerging communication and network technologies enable improved efficiency, convenience, and connectivity in various domains. However, these advancements also pose significant security risks due to the increasing complexity of attacks and the lack of effective security strategies. Traditional security schemes, such as signature-based intrusion detection systems, have limited predictive power and require a considerable number of cybersecurity staff. Therefore, proactive defense techniques based on network security situation prediction are essential. Network security situation prediction aims to forecast the future state of network security based on historical data and current observations. However, current prediction methods can only capture either long-term or short-term data features, leading to unsatisfactory results. In contrast, this paper proposes a novel attention-based long and short-period network security situation prediction (ALSNAP) scheme that combines improved bidirectional long short-term memory, vector autoregression, and multi-scale convolution with branching attention. It applies an attention mechanism to optimize the prediction model and enhance its accuracy. We evaluated ALSNAP on four public network datasets, including three typical IoT datasets, and compared it with other state-of-the-art methods. Our results demonstrate that ALSNAP achieves higher prediction accuracy than other methods. We also analyzed the potential applications of our scheme to real networks and its integration into intelligent network security situation awareness systems to deliver timely and effective countermeasures.

Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks.

We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model. The introduction of a clock-cycle for hidden units allows the model to learn short-term information from high-frequency update modules while retaining long-term memory from low-frequency update modules, thereby enhancing the model's ability to capture data patterns. Experimental results demonstrate that the optimized clock-cycle RNN outperforms other network models in extracting the temporal and nonlinear features of network security situations, leading to improved prediction accuracy. Furthermore, our approach has low time complexity and excellent real-time performance, ideal for monitoring large-scale network traffic in sensor networks.

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

ISSA-ELM: A Network Security Situation Prediction Model

To resolve the problems of low prediction accuracy and slow convergence speed of traditional extreme learning machines in network security situation prediction methods, we combine a meta-heuristic search algorithm with neural networks and propose a prediction method based on the improved sparrow search algorithm optimization of an extreme learning machine. Firstly, the initial population is initialized by cat-mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm. Secondly, the Cauchy mutation and tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. The improvement not only guarantees the diversity of the population, but also makes up for the defect that the sparrow search algorithm is easily trapped in the local optima in later iterations, and greatly improves the accuracy of the network security situation prediction.

Information security of flowmeter communication network based on multi-sensor data fusion

With the progress of society, the use of intelligent flowmeters has become more and more widespread. The powerful functions of intelligent flowmeters have made people’s production and living more convenient. Still, at the same time, public data, including industry and agriculture privacy and information security, have exposed huge security risks. This paper aims to study the information security of flowmeter communication networks based on multi-sensor data fusion and artificial intelligence-driven. The present study introduces network information security and its development direction, proposes a cloud computing-based network security situation prediction algorithm, uses the cloud model to predict the security situation of the cloud computing network, and gives a cloud computing network security situation assessment system model. At the same time, it analyzes and explores network information security, and explores network security based on multi-sensor data fusion and artificial intelligence driving. The experimental research results show that due to the unique advantages of the universal flowmeter intelligent processing system, such as high resource utilization, uniform standards, and wide application range, it has a lot of room for development. With the development of science and technology and application fields, and the continuous progress and expansion of general flowmeters, the future space of general category flowmeters is huge. The popularity of the Internet is getting faster and faster, and the requirements for information security are getting higher and higher, and the information security issues faced in the future will become more and more complicated. Therefore, it is necessary to speed up the construction of an information security system, and take measures to strictly manage from the aspects of law, management, technology and talents.

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.

Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network. Finally, a network security situation prediction model based on EMD-ELPSO-BiGRU is constructed to predict each intrinsic mode function, respectively, and the prediction results are superimposed to obtain the final network security situation prediction value. Simulation results show that ELPSO has better optimization performance, and EMD-ELPSO-BiGRU model has higher prediction accuracy and significantly improved convergence speed compared with other traditional 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.

Prediction of Industrial Network Security Situation Based on Noise Reduction Using EMD

Industrial Internet security is a prerequisite to ensure the high-quality development of the Industrial Internet. The significant way to curb Industrial Internet security accidents and prevent cyber threats proactively is effectively controlling the changes in network situations. In this paper, we propose a new prediction model based on Long Short-Term Memory (LSTM), minimum mean square variance criterion (MMSVC), and empirical mode decomposition (EMD), with the aim of effective noise reduction and high prediction accuracy. To minimize the disturbance of random noise, we firstly deleted several outliers in high-frequency and noisy Intrinsic Mode Functions (IMFs) decomposed by EMD. MMSVC performs well in identifying noisy IMFs without using thresholds. For the blank places, we refilled them by a certain weight with relevant figures. After that, the LSTM model was applied to predict the denoised signal. The preliminary experimental analysis illustrated that noise reduction with the EMD method could provide a significant boost in forecasting performance.

Prediction of Computer Network Security Situation Based on Association Rules Mining

Traditional NSSA (network security situational awareness) systems have significant equipment limitations, poor data fusion capabilities, and a low level of analysis and evaluation, making them difficult to adapt to large-scale and complex network environments. This paper proposes the study of computer NSS (network security situation) prediction technology based on AR (association rules) mining to solve this problem. The support-confidence framework is improved by introducing an interest evaluation standard, and the value of AR is re-evaluated, based on a discussion of traditional concepts and algorithms related to AR mining. The MFP-interest algorithm proposed in this paper is a combination of alarm AR template and interest degree. The MFP-interest algorithm was put to the test. We discovered that the MFP-interest algorithm can effectively predict NSS and indicate its development trend when run in a real-world network environment. Most time points have a relative error range of less than 0.035.

Research on prediction of school computer network security situation based on IOT

Research on prediction of school computer network security situation based on IOT

Network security situation prediction based on feature separation and dual attention mechanism

With the development of smart cities, network security has become more and more important. In order to improve the safety of smart cities, a situation prediction method based on feature separation and dual attention mechanism is presented in this paper. Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. Then, we propose a feature separation method, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged. Finally, limited attention is proposed according to global attention. We sum the outputs of the two attention modules to form a dual attention mechanism, which can improve feature representation. Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. In other words, the technology can help smart cities predict network attacks more accurately.