Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

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.

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Design of Power Communication Data Network Management System and Implementation of Data Acquisition Module

The power communication network is a separate network from the power grid whose primary purpose is to ensure the power grid's safe operation.This paper expounds the composition of the comprehensive network management architecture of the power communication data network and the implementation of the data acquisition module in the network management system through theoretical analysis,for the reference of relevant personnel,in order to better promote the collection of power grid communication network data.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

Research on Data Tampering Prevention Method for ATC Network Based on Zero Trust

The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect,which is easy to leads to the problem that the data is usurped.Starting from the application of the ATC(automatic train control)network,this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data.Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation,this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of ATC’s information sharing on the Internet.From the single management authority to the unified management of data units,the systematic algorithm improvement of shared network data tamper prevention method is realized,and RDTP(Reliable Data Transfer Protocol)is selected in the network data of information sharing resources to realize the effectiveness of tamper prevention of air traffic control data during transmission.The results show that this method can reasonably avoid the tampering of information sharing on the Internet,maintain the security factors of air traffic control information sharing on the Internet,and the Central Processing Unit(CPU)utilization rate is only 4.64%,which effectively increases the performance of air traffic control data comprehensive security protection system.

Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

GraphCWGAN-GP:A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.

Energy-Aware Traffic Routing with Named Data Networking

Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate a- daption, and also predict traffic matrices to pre- serve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to fred its so- lution from a novel architecture called Named Data Networking （NDN） which tent in edge routers and can flexibly cache con- decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider green- ing idea in the design of future Internet.

Energy-balanced clustering protocol for data gathering in wireless sensor networks with unbalanced traffic load

Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.

Two States CBR Modeling of Data Source in Dynamic Traffic Monitoring Sensor Networks

Real traffic information was analyzed in the statistical characteristics and approximated as a Gaussian time series. A data source model, called two states constant bit rate (TSCBR), was proposed in dynamic traffic monitoring sensor networks. Analysis of autocorrelation of the models shows that the proposed TSCBR model matches with the statistical characteristics of real data source closely. To further verify the validity of the TSCBR data source model, the performance metrics of power consumption and network lifetime was studied in the evaluation of sensor media access control (SMAC) algorithm. The simulation results show that compared with traditional data source models, TSCBR model can significantly improve accuracy of the algorithm evaluation.

Traffic Scheduling Mechanism Based on Interference Avoidance for Meter Data Collection in Wireless Smart Grid Communication Networks

Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Mechanical movement data acquisition method based on the multilayer neural networks and machine vision in a digital twin environment[version 1;peer review:2 approved]

Background:Digital twin requires virtual reality mapping and optimization iteration between physical devices and virtual models.The mechanical movement data collection of physical equipment is essential for the implementation of accurate virtual and physical synchronization in a digital twin environment.However,the traditional approach relying on PLC(programmable logic control)fails to collect various mechanical motion state data.Additionally,few investigations have used machine visions for the virtual and physical synchronization of equipment.Thus,this paper presents a mechanical movement data acquisition method based on multilayer neural networks and machine vision.Methods:Firstly,various visual marks with different colors and shapes are designed for marking physical devices.Secondly,a recognition method based on the Hough transform and histogram feature is proposed to realize the recognition of shape and color features respectively.Then,the multilayer neural network model is introduced in the visual mark location.The neural network is trained by the dropout algorithm to realize the tracking and location of the visual mark.To test the proposed method,1000 samples were selected.Results:The experiment results shows that when the size of the visual mark is larger than 6mm,the recognition success rate of the recognition algorithm can reach more than 95%.In the actual operation environment with multiple cameras,the identification points can be located more accurately.Moreover,the camera calibration process of binocular and multi-eye vision can be simplified by the multilayer neural networks.Conclusions:This study proposes an effective method in the collection of mechanical motion data of physical equipment in a digital twin environment. Further studies are needed to perceive posture and shape data of physical entities under the multi-camera redundant shooting.

A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent tragic system （ITS）. In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of tragic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation （BP） neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.

Traffic flow prediction based on BILSTM model and data denoising scheme

Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.

Real traffic-data based evaluation of vehicular traffic environment and state- of-the-art with future issues in location-centric data dissemination for VANETs

Extensive investigation has been performed in location-centric or geocast routing protocols for reliable and efficient dissemination of information in Vehicular Adhoc Networks （VANETs）. Various location-centric routing protocols have been suggested in literature for road safety ITS applications considering urban and highway traffic environment. This paper characterizes vehicular environments based on real traffic data and investigates the evolution of location-centric data dissemination. The current study is carded out with three main objectives： （i） to analyze the impact of dynamic traffic environment on the design of data dissemination techniques, （ii） to characterize location-centric data dissemination in terms of functional and qualitative behavior of protocols, properties, and strengths and weaknesses, and （iii） to find some future research directions in information dissemination based on location. Vehicular traffic environments have been classified into three categories based on physical characteristics such as speed, inter-vehicular distance, neighborhood stability, traffic volume, etc. Real traffic data is considered to analyze on-road traffic environments based on the measurement of physical parameters and weather conditions. Design issues are identified in incorporating physical parameters and weather conditions into data dissemination. Functional and qualitative characteristics of location-centric techniques are explored considering urban and highway environments. Comparative analysis of location-centric techniques is carded out for both urban and highway environments individually based on some unique and common characteristics of the environments. Finally, some future research directions are identified in the area based on the detailed investigation of traffic environments and location-centric data dissemination techniques.

分支河流体系沉积学工作框架与流程

基于现有的研究成果和存在的问题,探讨了分支河流体系(DFS)研究中的关键科学问题、主要研究内容、研究方法和工作流程。研究结果表明:①DFS研究中最关键的3个科学问题是明确河网结构和河型演变规律、构建沉积标志和沉积模式、分析其形成和分布的控制因素。②DFS研究的主要内容包括建设形态沉积学数据库、现代沉积机理研究、分类研究、建立沉积模式、储层建模与储层预测等5个方面。③DFS研究中的关键技术包括基于遥感图像的形态数据采集、形成机理的水槽和模拟实验、河网重构、顶点位置预测与河道分汊点自动生成方法、储层建模知识库平台等。④DFS研究的基本工作流程是先建立形态沉积学数据库,搭建数据库软件平台,在此基础上选择具有代表性的DFS进行现代沉积解剖,然后综合现代沉积调查、露头解剖和模拟实验成果,形成分类体系,总结各类DFS的识别标志和沉积模式,分层次建立储层预测模型,形成沉积结构储层预测模型的建模软件平台,从而预测沉积体系中有利储层的分布。

基于多层级时空图神经网络的风电机组在线异常检测

在风电场运营中,准确及时的故障检测是降低风电机组运行维护成本的关键。然而,现有检测方法未充分挖掘功能单元间的潜在时空关联,限制了检测准确性的提升。文中提出了一种基于多层级时空图神经网络的风电机组在线异常检测方法,以提高故障检测的准确性。该方法依据风电机组物理结构,将其功能单元划分为多个子图,从而构筑了一个多层级的时空图神经网络,通过图注意力机制和多头注意力机制全方位地分析风电机组各传感器节点与功能单元之间的关联强度。同时,针对数据采集与监控(SCADA)系统数据的时间关联,设计了动态图神经网络和时间注意力机制,使正常行为预测模型捕捉了SCADA系统数据的时间关联特性,实现了空间和时间特性的有效融合。最后,基于中国上海某风电场的实际数据验证了所提方法的显著有效性。