Network Security Situation Research Articles

Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

Aims and background: Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection. Objectives and Methods: Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules. Results: The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data. Conclusions: In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

A Novel Fractional Model and Its Application in Network Security Situation Assessment

The evaluation process of the Fractional Order Model is as follows. To address the commonly observed issue of low accuracy in traditional situational assessment methods, a novel evaluation algorithm model, the fractional-order BP neural network optimized by the chaotic sparrow search algorithm (TESA-FBP), is proposed. The fractional-order BP neural network, by incorporating fractional calculus, demonstrates enhanced dynamic response characteristics and historical dependency, showing exceptional potential for handling complex nonlinear problems, particularly in the field of network security situational awareness. However, the performance of this network is highly dependent on the precise selection of network parameters, including the fractional order and initial values of the weights. Traditional optimization methods often suffer from slow convergence, a tendency to be trapped in local optima, and insufficient optimization accuracy, which significantly limits the practical effectiveness of the fractional-order BP neural network. By introducing cubic chaotic mapping to generate an initial population with high randomness and global coverage capability, the exploration ability of the sparrow search algorithm in the search space is effectively enhanced, reducing the risk of falling into local optima. Additionally, the Estimation of Distribution Algorithm (EDA) constructs a probabilistic model to guide the population toward the globally optimal region, further improving the efficiency and accuracy of the search process. The organic combination of these three approaches not only leverages their respective strengths, but also significantly improves the training performance of the fractional-order BP neural network in complex environments, enhancing its generalization ability and stability. Ultimately, in the network security situational awareness system, this integration markedly enhances the prediction accuracy and response speed.

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

A Survey on Network Security Traffic Analysis and Anomaly Detection Techniques

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

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 assessment method based on fusion model

This research introduces an innovative network security situation assessment (NSSA) model, designed to overcome the shortcomings in feature extraction quality and efficiency observed in existing methods. At the core of this model is a fusion model (FM), which uniquely combines an attention mechanism with a bi-directional gated recurrent unit (BiGRU). This FM framework is adept at extracting pivotal information pertinent to various cyber threats. It employs the attention mechanism to assign appropriate weights to these crucial features, thereby elevating the model’s precision. The BiGRU, in synergy with newly proposed quantitative indicators, is responsible for generating the final evaluation results, offering a more refined measure of the cybersecurity stance. Comparative threat detection experiments reveal that the FM model exhibits superior performance across multiple evaluation metrics, marking a significant advancement in the field of network security assessment.

Research on Network Security Situation Awareness Technology Based on Security Intelligent Monitoring Technology

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors.

Retraction Note: Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction

Retraction Note: Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction

Detection of Geographic Information System Security Hazards in the IoT Based on Network Security Situation Awareness

Abstract The geographic information system (GIS) is the important system these days for navigation and directing the routes. In order to detect the security risks of the GIS efficiently and accurately and to protect the security of the GIS, a GIS security risk detection method in the Internet of Things based on network security situational awareness has been proposed. The data preprocessing method based on the operation data of the GIS is adopted. The normalized and standardized processing of the operation data of the GIS is devised to reduce the dimensional difference between the data and the geographical areas. This will facilitate the subsequent applications of the GIS to make better decisions by preserving the security. The redundant data in the GIS operation data are removed by using feature reduction algorithms through the acquisition method. The security elements based on enhanced probabilistic neural network are inferred, and the useful data samples are obtained as the security elements detection set. A rough set theory is also used to realize geographic security hidden danger identification. The experimental results show that the proposed method is efficient to maintain security and to detect the geographic security risk detection within a short span of time. The detection result accuracy is high, which meets the GIS application requirements.

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 awareness of security situation information security measurement method based on data mining

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation.

Deep learning with blockchain based cyber security threat intelligence and situational awareness system for intrusion alert prediction

Network security situation assessment (NSSA) is imperative and active defense technology in the network security situation. By examining NSSA data, one can examine the threat of network security and examine the network attack phase and hence fully grasp the complete network security situation. With the quick design of 5 G, the cloud model and Internet of things (IoT), the network platform is increasingly complicated and resulting in diversity of network threats which discover the accuracy. Thus, a new blockchain based cyber-security threat intelligence (CTI) and situational awareness system is devised for intrusion alert prediction. A blockchain-based CTI model is considered where data acquired are allowed to linear normalization. Here, the cyber situational awareness engine is used for alert segregation, which is implemented with entropy weighting power k means algorithm wherein weights generated during alert segregation are updated using Adaptive Transit Search (ATS). Then, the feature selection is implemented using hybrid Soergel and Lorentzian. The selected features are fed to Deep Maxout Network (DMN) for performing intrusion alert prediction. Finally, the cyber attack mitigation is carried out by blacklisting based on predicted result. The modified DMN outperformed with highest F-measure of 95.2%, precision of 96.9% and recall of 94.7%.

Intelligent Prediction of Network Security Situations based on Deep Reinforcement Learning Algorithm

The limitations of traditional network security assessment methods characterized by manual definitions and measurements, data overload, poor performance, and non-negligible drawbacks are addressed in this research. A novel network security system employing a deep learning algorithm is proposed to overcome these challenges. The research unfolds in three key phases. First, a deep self-encoding model is developed to distinguish various network attacks effectively. Subsequently, the creation of missing measurement weights enhances pattern detection, even when dealing with a limited number of training samples. Finally, the model assesses and computes attack issues, assigns impact scores to each attack, and determines the overall network security value. Experimental results demonstrate that the deep auto encoder-based deep neural network (DAEDNN), in conjunction with the proposed unique oversampling weighting (UOSW) algorithm, significantly outperforms traditional methods such as decision trees (DT), support vector machines (SVM), and long short-term memory (LSTM) models. The F1 score of UOSW surpasses these models by approximately 2.77, 10.5, and 5.2, respectively. The deep self-encoding model employed in the proposed system offers superior accuracy and recall rates, leading to more precise and efficient measurement results.

Estimating computer network security scenarios with association rules

Traditional NSSA (network security situational awareness) systems are problems familiarize to enormous for complicated network circumstances due to instrument limitations, inadequate and data fusion capabilities. This paper proposes the investigation of Association Rules (AR) based NSS (network security situation) prediction technology. Mining for regulations for associations to remedy this issue re- confidence support framework is enhanced by incorporating an interest. The assessment standard is revised, and the worth of AR is reassessed established on a discussion of associated standard ideas and algorithms AR mining. This research proposes an MFP-interest algorithm that combines an alert AR pattern with a degree of interest. The MFP-interest algorithm was evaluated. The researcher found that the MFP-interest algorithm is capable of accurately predicting NSS. The majority of time points have relative error ranges of less than 0.035.

An intrusion detection system based on convolution neural network.

With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system's feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

A Precise sensing method of campus network security situation based on fuzzy clustering algorithm

A Precise sensing method of campus network security situation based on fuzzy clustering algorithm

Communication network security situation analysis based on time series data mining technology

Communication network security situation analysis based on time series data mining technology