The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage de...The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.展开更多
Intrusion detection system ean make effective alarm for illegality of networkusers, which is absolutely necessarily and important to build security environment of communicationbase service According to the principle t...Intrusion detection system ean make effective alarm for illegality of networkusers, which is absolutely necessarily and important to build security environment of communicationbase service According to the principle that the number of network traffic can affect the degree ofself-similar traffic, the paper investigates the variety of self-similarity resulted fromunconventional network traffic. A network traffic model based on normal behaviors of user isproposed and the Hursl parameter of this model can be calculated. By comparing the Hurst parameterof normal traffic and the self-similar parameter, we ean judge whether the network is normal or notand alarm in time.展开更多
This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context...This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context of the first traffic light in London in 1868 to the modern automated systems,the study explores the complexity and adaptability of traffic lights in Shanghai.Through field surveys and interviews with traffic engineers,the paper debunks common misconceptions about traffic light operation,revealing a sophisticated network that responds to real-time traffic dynamics using software like the Sydney Coordinated Adaptive Traffic System(SCATS)6.The study also discusses the importance of pedestrian safety,suggesting future enhancements such as Global Positioning System(GPS)based emergency systems and accommodations for color-blind individuals.The paper further delves into the potential of Artificial Intelligence(AI)and Vehicle-to-Infrastructure(V21)technology in revolutionizing traffic light systems,emphasizing their role in improving traffic flow and safety.The findings underscore Shanghai’s progressive approach to traffic management,showcasing the city’s commitment to optimizing traffic control solutions for the benefit of both vehicles and pedestrians.展开更多
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based approaches.Among these,multimodal learning-based classification methods...With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based approaches.Among these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification accuracy.However,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the data.To address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction.Specifically,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective learning.This continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network conditions.Experimental results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.展开更多
Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract loc...Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features;Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection model based on parallel dilated convolution and residual learning (Res-PDC). To better explore the interactive relationships between features, the traffic samples are converted into two-dimensional matrix. A module combining parallel dilated convolutions and residual learning (res-pdc) was designed to extract local and global features of traffic at different scales. By utilizing res-pdc modules with different dilation rates, we can effectively capture spatial features at different scales and explore feature dependencies spanning wider regions without increasing computational resources. Secondly, to focus and integrate the information in different feature subspaces, further enhance and extract the interactions among the features, multi-head attention is added to Res-PDC, resulting in the final model: multi-head attention enhanced parallel dilated convolution and residual learning (MHA-Res-PDC) for network traffic anomaly detection. Finally, comparisons with other machine learning and deep learning algorithms are conducted on the NSL-KDD and CIC-IDS-2018 datasets. The experimental results demonstrate that the proposed method in this paper can effectively improve the detection performance.展开更多
Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researche...Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researchers have proposed various machine learning and deep learning approaches to tackle this challenge.However,existing mainstream methods face several general issues.On one hand,the widely used Transformer architecture exhibits high computational complexity,which negatively impacts its efficiency.On the other hand,traditional methods are often unreliable in traffic representation,frequently losing important byte information while retaining unnecessary biases.To address these problems,this paper introduces the Swin Transformer architecture into the domain of network traffic classification and proposes the NetST(Network Swin Transformer)model.This model improves the Swin Transformer to better accommodate the characteristics of network traffic,effectively addressing efficiency issues.Furthermore,this paper presents a traffic representation scheme designed to extract meaningful information from large volumes of traffic while minimizing bias.We integrate four datasets relevant to network traffic classification for our experiments,and the results demonstrate that NetST achieves a high accuracy rate while maintaining low memory usage.展开更多
In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in ...In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.展开更多
This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the ...This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.展开更多
In a given district, the accessibility of any point should be the synthetically evaluation of the internal and external accessibilities. Using MapX component and Delphi, the author presents an information system to ca...In a given district, the accessibility of any point should be the synthetically evaluation of the internal and external accessibilities. Using MapX component and Delphi, the author presents an information system to calculate and analyze regional accessibility according to the shortest travel time, generating thus a mark diffusing figure. Based on land traffic network, this paper assesses the present and the future regional accessibilities of sixteen major cities in the Yangtze River Delta. The result shows that the regional accessibility of the Yangtze River Delta presents a fan with Shanghai as its core. The top two most accessible cities are Shanghai and Jiaxing, and the bottom two ones are Taizhou （Zhejiang province） and Nantong With the construction of Sutong Bridge, Hangzhouwan Bridge and Zhoushan Bridge, the regional internal accessibility of all cities will be improved. Especially for Shaoxing, Ningbo and Taizhou （Jiangsu province）, the regional internal accessibility will be decreased by one hour, and other cities will be shortened by about 25 minutes averagely. As the construction of Yangkou Harbor in Nantong, the regional external accessibility of the harbor cities in Jiangsu province will be speeded up by about one hour.展开更多
This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This meth...This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.展开更多
Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which t...Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi...Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.展开更多
The flourishing complex network theory has aroused increasing interest in studying the properties of real-world networks. Based on the traffic network of Chang-Zhu Tan urban agglomeration in central China, some basic ...The flourishing complex network theory has aroused increasing interest in studying the properties of real-world networks. Based on the traffic network of Chang-Zhu Tan urban agglomeration in central China, some basic network topological characteristics were computed with data collected from local traffic maps, which showed that the traffic networks were small-world networks with strong resilience against failure; more importantly, the investigations of as- sortativity coefficient and average nearestlneighbour degree implied the disassortativity of the traffic networks. Since traffic network hierarchy as an important basic property has been neither studied intensively nor proved quantitatively, the authors are inspired to analyse traffic network hierarchy with disassortativity and to finely characterize hierarchy in the traffic networks by using the n-degree-n-clustering coefficient relationship. Through numerical results and analyses an exciting conclusion is drawn that the traffic networks exhibit a significant hierarchy, that is, the traffic networks are proved to be hierarchically organized. The result provides important information and theoretical groundwork for optimal transport planning.展开更多
Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abno...Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification.Traditional methods for network traffic classification utilize algorithms such as Naive Bayes,Decision Tree and XGBoost.However,network traffic classification,which is required for network behavior identification,generally suffers from the problem of low accuracy even with the recently proposed deep learning models.To improve network traffic classification accuracy thus improving network intrusion detection rate,this paper proposes a new network traffic classification model,called ArcMargin,which incorporates metric learning into a convolutional neural network(CNN)to make the CNN model more discriminative.ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible.The metric learning regularization feature is called additive angular margin loss,and it is embedded in the object function of traditional CNN models.The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms.According to a set of classification indicators,the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks.Moreover,in open-set tasks,the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.展开更多
Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and tem...Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and temporal dynamics. The fluctuations of airport arrival and departure traffic are studied from the point of view of networks as the special correlation between different airports. Our collected flow volume data on the time-dependent activity of US airport arrival and departure traffic indicate that the coupling between the average flux and the fluctuation of an individual airport obeys a certain scaling law with a wide variety of scaling exponents between 1/2 and 1. These scaling phenomena can explain the interaction between the airport internal dynamics （e.g. queuing at airports, a ground delay program and following flying traffic） and a change in the external （network-wide） traffic demand （e.g. an increase in traffic during peak hours every day）, allowing us to further understand the mechanisms governing the collective behaviour of the transportation system. We separate internal dynamics from external fluctuations using a scaling law which is helpful for us to systematically determine the origin of fluctuations in airport arrival and departure traffic, uncovering the collective dynamics. Hot spot features are observed in airport traffic data as the dynamical inhomogeneity in the fluxes of individual airports. The intrinsic characteristics of airport arrival and departure traffic under severe weather is discussed as well.展开更多
The modeling of network traffic is important for the design and application of networks, but little is known as to the characteristics of distribution of packets in network traffic. In this letter the distribution of ...The modeling of network traffic is important for the design and application of networks, but little is known as to the characteristics of distribution of packets in network traffic. In this letter the distribution of packets in network traffic is explored.展开更多
Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of servi...Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.展开更多
This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstruc...This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
文摘The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.
文摘Intrusion detection system ean make effective alarm for illegality of networkusers, which is absolutely necessarily and important to build security environment of communicationbase service According to the principle that the number of network traffic can affect the degree ofself-similar traffic, the paper investigates the variety of self-similarity resulted fromunconventional network traffic. A network traffic model based on normal behaviors of user isproposed and the Hursl parameter of this model can be calculated. By comparing the Hurst parameterof normal traffic and the self-similar parameter, we ean judge whether the network is normal or notand alarm in time.
文摘This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context of the first traffic light in London in 1868 to the modern automated systems,the study explores the complexity and adaptability of traffic lights in Shanghai.Through field surveys and interviews with traffic engineers,the paper debunks common misconceptions about traffic light operation,revealing a sophisticated network that responds to real-time traffic dynamics using software like the Sydney Coordinated Adaptive Traffic System(SCATS)6.The study also discusses the importance of pedestrian safety,suggesting future enhancements such as Global Positioning System(GPS)based emergency systems and accommodations for color-blind individuals.The paper further delves into the potential of Artificial Intelligence(AI)and Vehicle-to-Infrastructure(V21)technology in revolutionizing traffic light systems,emphasizing their role in improving traffic flow and safety.The findings underscore Shanghai’s progressive approach to traffic management,showcasing the city’s commitment to optimizing traffic control solutions for the benefit of both vehicles and pedestrians.
基金supported by the National Key Research and Development Program of China No.2023YFB2705000.
文摘With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based approaches.Among these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification accuracy.However,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the data.To address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction.Specifically,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective learning.This continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network conditions.Experimental results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.
基金supported by the Xiamen Science and Technology Subsidy Project(No.2023CXY0318).
文摘Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features;Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection model based on parallel dilated convolution and residual learning (Res-PDC). To better explore the interactive relationships between features, the traffic samples are converted into two-dimensional matrix. A module combining parallel dilated convolutions and residual learning (res-pdc) was designed to extract local and global features of traffic at different scales. By utilizing res-pdc modules with different dilation rates, we can effectively capture spatial features at different scales and explore feature dependencies spanning wider regions without increasing computational resources. Secondly, to focus and integrate the information in different feature subspaces, further enhance and extract the interactions among the features, multi-head attention is added to Res-PDC, resulting in the final model: multi-head attention enhanced parallel dilated convolution and residual learning (MHA-Res-PDC) for network traffic anomaly detection. Finally, comparisons with other machine learning and deep learning algorithms are conducted on the NSL-KDD and CIC-IDS-2018 datasets. The experimental results demonstrate that the proposed method in this paper can effectively improve the detection performance.
基金supported by National Natural Science Foundation of China(62473341)Key Technologies R&D Program of Henan Province(242102211071,252102211086,252102210166).
文摘Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researchers have proposed various machine learning and deep learning approaches to tackle this challenge.However,existing mainstream methods face several general issues.On one hand,the widely used Transformer architecture exhibits high computational complexity,which negatively impacts its efficiency.On the other hand,traditional methods are often unreliable in traffic representation,frequently losing important byte information while retaining unnecessary biases.To address these problems,this paper introduces the Swin Transformer architecture into the domain of network traffic classification and proposes the NetST(Network Swin Transformer)model.This model improves the Swin Transformer to better accommodate the characteristics of network traffic,effectively addressing efficiency issues.Furthermore,this paper presents a traffic representation scheme designed to extract meaningful information from large volumes of traffic while minimizing bias.We integrate four datasets relevant to network traffic classification for our experiments,and the results demonstrate that NetST achieves a high accuracy rate while maintaining low memory usage.
基金supported in part by the Korea Research Institute for Defense Technology Planning and Advancement(KRIT)funded by the Korean Government’s Defense Acquisition Program Administration(DAPA)under Grant KRIT-CT-21-037in part by the Ministry of Education,Republic of Koreain part by the National Research Foundation of Korea under Grant RS-2023-00211871.
文摘In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.
文摘This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.
基金National Natural Science Foundation of China, No.40371044 No.70573053
文摘In a given district, the accessibility of any point should be the synthetically evaluation of the internal and external accessibilities. Using MapX component and Delphi, the author presents an information system to calculate and analyze regional accessibility according to the shortest travel time, generating thus a mark diffusing figure. Based on land traffic network, this paper assesses the present and the future regional accessibilities of sixteen major cities in the Yangtze River Delta. The result shows that the regional accessibility of the Yangtze River Delta presents a fan with Shanghai as its core. The top two most accessible cities are Shanghai and Jiaxing, and the bottom two ones are Taizhou （Zhejiang province） and Nantong With the construction of Sutong Bridge, Hangzhouwan Bridge and Zhoushan Bridge, the regional internal accessibility of all cities will be improved. Especially for Shaoxing, Ningbo and Taizhou （Jiangsu province）, the regional internal accessibility will be decreased by one hour, and other cities will be shortened by about 25 minutes averagely. As the construction of Yangkou Harbor in Nantong, the regional external accessibility of the harbor cities in Jiangsu province will be speeded up by about one hour.
基金supported in part by the National Natural Science Foundation of China under Grant No.61071126the National Radio Project under Grants No. 2010ZX03004001, No.2010ZX03004-002, No.2011ZX03002001
文摘This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.
文摘Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.
基金supported by the National Natural Science Foundation of China (Grant No. 60964006)the Scientific Research Innovation Fund Project for Graduate Student of Hunan,China (Grant No.3340-74236000003)the Open Program of State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University),China (Grant No.2007K-0027)
文摘The flourishing complex network theory has aroused increasing interest in studying the properties of real-world networks. Based on the traffic network of Chang-Zhu Tan urban agglomeration in central China, some basic network topological characteristics were computed with data collected from local traffic maps, which showed that the traffic networks were small-world networks with strong resilience against failure; more importantly, the investigations of as- sortativity coefficient and average nearestlneighbour degree implied the disassortativity of the traffic networks. Since traffic network hierarchy as an important basic property has been neither studied intensively nor proved quantitatively, the authors are inspired to analyse traffic network hierarchy with disassortativity and to finely characterize hierarchy in the traffic networks by using the n-degree-n-clustering coefficient relationship. Through numerical results and analyses an exciting conclusion is drawn that the traffic networks exhibit a significant hierarchy, that is, the traffic networks are proved to be hierarchically organized. The result provides important information and theoretical groundwork for optimal transport planning.
基金This work was supported by the National Natural Science Foundation of China(61871046).
文摘Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification.Traditional methods for network traffic classification utilize algorithms such as Naive Bayes,Decision Tree and XGBoost.However,network traffic classification,which is required for network behavior identification,generally suffers from the problem of low accuracy even with the recently proposed deep learning models.To improve network traffic classification accuracy thus improving network intrusion detection rate,this paper proposes a new network traffic classification model,called ArcMargin,which incorporates metric learning into a convolutional neural network(CNN)to make the CNN model more discriminative.ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible.The metric learning regularization feature is called additive angular margin loss,and it is embedded in the object function of traditional CNN models.The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms.According to a set of classification indicators,the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks.Moreover,in open-set tasks,the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61039001)
文摘Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and temporal dynamics. The fluctuations of airport arrival and departure traffic are studied from the point of view of networks as the special correlation between different airports. Our collected flow volume data on the time-dependent activity of US airport arrival and departure traffic indicate that the coupling between the average flux and the fluctuation of an individual airport obeys a certain scaling law with a wide variety of scaling exponents between 1/2 and 1. These scaling phenomena can explain the interaction between the airport internal dynamics （e.g. queuing at airports, a ground delay program and following flying traffic） and a change in the external （network-wide） traffic demand （e.g. an increase in traffic during peak hours every day）, allowing us to further understand the mechanisms governing the collective behaviour of the transportation system. We separate internal dynamics from external fluctuations using a scaling law which is helpful for us to systematically determine the origin of fluctuations in airport arrival and departure traffic, uncovering the collective dynamics. Hot spot features are observed in airport traffic data as the dynamical inhomogeneity in the fluxes of individual airports. The intrinsic characteristics of airport arrival and departure traffic under severe weather is discussed as well.
文摘The modeling of network traffic is important for the design and application of networks, but little is known as to the characteristics of distribution of packets in network traffic. In this letter the distribution of packets in network traffic is explored.
文摘Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.
基金Project supported in part by the National High Technology Research and Development Program of China (Grant No. 2007AA01Z480)
文摘This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.
