Network Traffic Distribution Research Articles

Study on Traffic Anomaly Detection of Wireless Communication Network Based on Fuzzy Relation Equation

As the core technology of intrusion detection system, network abnormal traffic detection has always been an important research direction in academia and industry. Related studies show that the failure to find the abnormal situation in the network in time will cause incalculable damage to the computer system and even the whole network. With the emergence of large-scale lightweight terminal nodes, whose characteristics of low computing power and continuous data collection, a distributed abnormal traffic detection technology has emerged. As a kind of distributed structure with decentralized data, federated learning can not only protect local data privacy, reduce communication overhead, but also achieve the effect of centralized training, However, in the era of the Internet of Things with heterogeneous network integration and regular access of massive terminals, the network traffic distribution of different devices is differentiated due to the different security needs of diversified terminals. This will lead to the traditional federated learning-based network anomaly traffic detection facing two major challenges, the uneven data distribution leads to the model training cannot be optimized, and the distributed training global model is not suitable for local network anomaly traffic detection. The scheme of this paper showed significant advantages in the performance evaluation of backbone and mission UAVs, achieving an accuracy of 92.47% and 93.01%, respectively. In contrast, the accuracy of the traditional federated learning method is 89.87% and 89.11%, respectively, which is slightly lower than the present scheme. We propose a framework and algorithm for network anomalous traffic detection based on cluster federated learning. Taking the Internet of Vehicles as the background, the security requirements of the devices connected to the Internet of Vehicles are analyzed, and a set of federated learning data sets meeting the distribution of network traffic in practical applications are constructed with the field recognized data set KDDCup99. This paper verifies the excellent performance of the network anomaly traffic detection mechanism based on cluster federated learning in the case of heterogeneous data distribution.

Revolutionizing MANET Route Discovery with INTSM: An Innovative Load Balancing Approach

Communication challenges in ad hoc networks arise due to the mobility of nodes, causing frequent changes in connections and locations. Maintaining network equilibrium to prevent node overload and underutilization is crucial. However, imposing static behaviors on nodes to improve performance can lead to delays, especially in core nodes. Addressing these issues, this research proposes the Intermediate Node Traffic Sharing Model (INTSM) for ad hoc networks. INTSM prioritizes congestion control and load balancing during route discovery, aiming to optimize network resource utilization and traffic distribution, thereby reducing packet delays. The model employs dynamic traffic sharing algorithms that consider real-time network conditions, enabling nodes to adjust their behaviour adaptively. This approach minimizes congestion by distributing traffic loads more evenly across the network, preventing bottlenecks at central nodes. Additionally, INTSM incorporates predictive analysis to foresee potential congestion points and reroute traffic proactively, enhancing overall network stability and performance. Extensive simulations demonstrate that INTSM significantly reduces average packet delay and improves throughput compared to traditional routing protocols. The results highlight the model's efficacy in diverse scenarios, including high mobility and varying traffic loads, proving its robustness and scalability. The primary objective of this study is to enhance navigation and equilibrium mechanisms to improve the performance of ad hoc networks, contributing to more reliable and efficient wireless communication systems. The findings of this research have significant implications for the design of future ad hoc networks, particularly in applications requiring high reliability and quick adaptation to changing network conditions, such as disaster recovery, military operations, and mobile sensor networks. By addressing the critical challenges of congestion control and load balancing, INTSM offers a promising solution to enhance the resilience and efficiency of ad hoc networks.

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection.

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

Penerapan Sistem Keamanan Jaringan dengan DNS Adguard ada Linux Debian dan Kontrol Akses Internet Berdasarkan Waktu Implementasi Pada Mikrotik Routerboard

This research aims to develop a network security system with AdGuard DNS on Debian Linux and control time-based internet access using the Microtik Routerboard. The main challenges are rising network security threats and the need for efficient internet access management in a variety of environments, including families,, education, and offices. The aim of this study is to improve network security and efficiency by blocking harmful content and advertisements, ining user privacy, and regulating internet access at a specific time. Technologies used include DNS AdGuard on Debian Linux to strengthen protection against network security threats and RouterOS Microtik devices to manage internet access based on time. These technologies include content filtering features, network traffic distribution (load balancing), and the ability to block unwanted sites to enhance network performance. Research results show that the combination of AdGuard DNS and Microtik Routerboard is an effective solution to enhance network security and efficiency. The system is capable of blocking harmful content and advertising, improving user privacy, and adjusting internet access as needed, thus creating a more secure, controlled, and optimal network.

A Network Intrusion Detection Method Based on Bagging Ensemble

The problems of asymmetry in information features and redundant features in datasets, and the asymmetry of network traffic distribution in the field of network intrusion detection, have been identified as a cause of low accuracy and poor generalization of traditional machine learning detection methods in intrusion detection systems (IDSs). In response, a network intrusion detection method based on the integration of bootstrap aggregating (bagging) is proposed. The extreme random tree (ERT) algorithm was employed to calculate the weights of each feature, determine the feature subsets of different machine learning models, then randomly sample the training samples based on the bootstrap sampling method, and integrated classification and regression trees (CART), support vector machine (SVM), and k-nearest neighbor (KNN) as the base estimators of bagging. A comparison of integration methods revealed that the KNN-Bagging integration model exhibited optimal performance. Subsequently, the Bayesian optimization (BO) algorithm was employed for hyper-parameter tuning of the base estimators’ KNN. Finally, the base estimators were integrated through a hard voting approach. The proposed BO-KNN-Bagging model was evaluated on the NSL-KDD dataset, achieving an accuracy of 82.48%. This result was superior to those obtained by traditional machine learning algorithms and demonstrated enhanced performance compared with other methods.

An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System

A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model, to enhance the quality of generated samples. The device can switch states to adapt to various network environments and has been experimentally validated for its effectiveness and feasibility.Moreover, employing an adaptive device, the samples of malicious traffic are adjusted. Experimental analysis demonstrates that the device significantly enhances the accuracy of anomaly traffic detection, improves robustness, and provides robust support for network security protection.

Mathematical Modeling and Analysis of Energy Aware Probabilistic Distribution Based Cluster Head Selection Algorithm for Wireless Sensor Networks

From a mathematical perspective, Wireless Sensor Networks (WSNs) are increasingly recognized for their utility across multiple sectors such as environmental oversight, healthcare monitoring, and process automation within industries. A paramount obstacle within WSNs is the management of energy efficiency, given that the sensor nodes are typically powered by batteries with finite energy reserves. The criticality of developing efficient routing protocols cannot be overstated, as they are instrumental in extending the operational lifespan of the network and guaranteeing dependable data communication. This study is centered around the mathematical modelling and performance evaluation of an innovative routing methodology that integrates machine learning algorithms to augment energy efficiency within WSNs. The novel routing strategy dynamically adjusts its operations in response to the immediate environmental and network states, aiming to minimize energy expenditure and elevate the network's overall efficiency. Through comprehensive numerical simulations, this research scrutinizes the efficacy of the machine learning-enhanced routing protocol against conventional routing methodologies, accentuating its advantages in energy savings and reliability in data transmission. The simulation framework encompasses a variety of network configurations, traffic distributions, and environmental contexts, employing metrics such as energy utilization, network longevity, packet delivery ratio, and latency to offer an in-depth examination of the machine learning-based routing approach's performance. Findings from the simulations affirm the algorithm's enhanced energy efficiency, which contributes to prolonged operation of sensor nodes and steadfast data communication across dynamically changing network landscapes. The implications of this study highlight the transformative potential of machine learning in redefining routing protocol design and optimization within energy-restricted WSNs. By elevating both energy efficiency and network functionality, this research marks a significant stride towards realizing sustainable and dependable WSNs, paving the way for their broader application in essential services.

Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: An empirical study in Ningbo, China.

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies.

A hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data

Traffic bottlenecks significantly influence the operation efficiency of large-scale road networks. Developing advanced control strategies for bottleneck optimization is a cost-efficient and critical way to deal with network congestion. However, the state-of-the-art studies on network congestion control focus on the topology level, which may fail to relieve congestion by addressing the root cause of bottleneck. This study proposed a hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data. First, the bottleneck-related sub-network (BRS) was identified by tracing vehicle trajectories upstream and downstream of the bottleneck based on the traffic flow propagation. Then, a hierarchical control framework was proposed for BRS optimization. Specifically, in the outer layer, i.e., the gating control layer, the multigated intersections in BRS were controlled via a multimemory deep Q-network approach to optimize the network traffic distribution. In the inner layer, i.e., the coordinated control layer, local intersection controllers were coordinated by adjusting the dynamic input and output streams of the bottleneck under the guidance of the outer layer controller, which helps balance the traffic pressure within BRS and avoids congestion transferring in the network. Both simulation and field experiments were conducted to verify the performance of the proposed hierarchical framework. Results reveal that the framework can effectively relieve network traffic congestion with decreased queue length and travel time.

Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services

Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services

Research on the evaluation of urban network traffic vitality distribution in the new media era——Take the analysis of Beijing's old urban areas based on microblog check-in data using QGIS operations as an example

At present, it is an era of rapid development of information technology. "Network traffic" is of great significance. It can be divided into "discussion traffic of topic quantity" and "user traffic of user quantity". In a comprehensive view, traffic is social resources. The larger the traffic, the higher the attention and influence it can bring, the stronger the ability to drive the benefits of all parties and occupy more social resources. Traffic value evaluation has become an indispensable work in the industry development process in the new media era. Starting from the attraction ability of network topic traffic at each location and relying on the microblog check-in data，by using QGIS analysis, this study evaluates the traffic distribution of each plot in the old urban area of Beijing and the distribution of social resources in each plot, in order to put forward a feasible method to evaluate the influence of plot use and promote the fair and balanced distribution of social resources, so as to provide reference ideas for urban vitality evaluation, urban resource allocation guidance and urban design.

IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR EFFECTIVE TRAFFIC DISTRIBUTION IN COMPUTER NETWORKS

The solution to the problem of efficient distribution of traffic in com-puter networks using Artificial Intelligence algorithms is presented. The prob-lem of effective distribution of traffic in computer networks is considered one of the key problems in the field of network interaction organization. It is very pivotal in the field of traffic management to increase the efficiency of using the existing channels and the quality of services provided to end users. Cur-rently, this problem has become more relevant, as the scope of its application has expanded, which now includes both policy enforcement systems and the field of information security. The paper considers different Artificial Intelli-gence algorithms that can be used for the efficient distribution of network traffic: Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN), as well as their advantages and disadvantages are presented. After features used to classify network traffic were analyzed, the best set of network traffic features was selected. Step-by-step testing of an in-telligent system for effective network traffic distribution was performed ac-cording to the testing scheme. A comparative analysis of the recorded results of the mentioned Artificial Intelligence algorithms and a reasonable choice is made. Based on the chosen Random Forest (RF) algorithm, an intelligent sys-tem for the effective distribution of network traffic was designed.

Rural financial service promotion mechanism based on data center network

Rural finance plays a central role in the rural economy. The development level of rural finance affects the economic development of rural areas. my country’s rural finance is obviously underdeveloped, and the existing promotion mechanism can be improved with the help of the financial data center network. This paper designs a rural financial service promotion mechanism based on the data center network. In order to deal with the problem of limited base station resources and difficulty in meeting user access requirements in rural environments, this paper adopts a cooperative transmission mechanism based on mobile devices. For the data center, in order to make full use of the multipath bandwidth of the data center network, the existing research mostly adopts the load balancing algorithm based on link awareness. However, these studies did not consider the non-uniform nature of data center network traffic size distribution. To this end, this paper designs a Utilization-aware Load balancing algorithm based on Flow Classification (UALBAFC). The algorithm analyzes traffic characteristics on the basis of realizing congestion awareness, and uses different strategies to allocate paths for large and small flows to achieve the best match between network traffic characteristics and the advantages of routing methods. In this paper, the simulation experiment evaluation of UALBAFC algorithm is carried out. The experimental results show that, compared with the existing schemes, the method in this paper can better realize the load balancing of the data center network in the data center network with unbalanced traffic size distribution.

Evaluation of Performance of different routing Algorithm using Noxim

One of the primary methods for examining and testing novel ideas in the Network-on-Chip space is simulation. Noxim simulator enables the analysis of performance for both established wired NoC and new WiNoC architectures. User can modify the size of the network, size of the packet, routing algorithm, various strategy selection and packet injection rate. The simulator evaluates NoCs in terms of various parameters of Network throughput, delay etc. In particular, the user can obtain various assessment metrics such as overall number of received packets/flits, global average throughput, max/min global delay, consumption of total energy. and so on. In comparison to traditional mesh NoC, this paper examines how different routing methods and WiNoC sizes affect network traffic distributions. We analyze PIR, Network throughput parameters of 8*8 mesh networks for various routing algorithms and its impacts are investigated. Simulation of Mesh wireless NOC architecture is validated.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.

Application of Fast P2P Traffic Recognition Technology Based on Decision Tree in the Detection of Network Traffic Data

With the rapid development of large-scale enterprise informatization construction, the network scale has become huge and complex, and the data traffic carried by the network is increasing. Accurate network traffic identification is the basis of network management and is of great significance to enterprise informatization construction and operation and maintenance. In response to the network operation and maintenance requirements of large enterprises, this paper analyzes the network architecture and network traffic distribution of large enterprise groups from the perspective of enterprise network operators and introduces the current operation and maintenance process of enterprise network performance failures. Maintenance process optimization and reengineering are carried out to plan and find out the shortcomings of the current process links and put forward corresponding solutions. Based on the research of traffic identification in recent years, a fast P2P network traffic anomaly identification algorithm based on decision tree model is proposed, which improves the efficiency and accuracy of network traffic application identification and network traffic anomaly data identification.

Performance and scalability evaluation of a permissioned Blockchain based on the Hyperledger Fabric, Sawtooth and Iroha

This paper shows the performance and scalability evaluation of different blockchain platform implementations. Hyperledger Iroha implementing YAC consensus, Sawtooth implementing PoET algorithm, and Hyperledger Fabric framework implementation. Performance evaluation and scalability assessment were done by varying different sets of parameters such as block size, transaction sending rate, network traffic distribution, and network size. Performance evaluation was done based on average transaction latency, network throughput, and transaction failure rate. Scalability was assessed based on changes in transaction latency and throughput with increasing network size. Test results let to study the impact of a particular parameter on the private blockchain network performance and show how they can be adjusted to improve performance.

Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure

Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests. Although Kubernetes’ horizontal pod autoscaler provides the resource autoscaling feature by monitoring the resource status of nodes and then making pod adjustments if necessary, it evenly allocates pods to worker nodes without considering the imbalance of resource demand between nodes in an edge computing environment. This paper proposes the traffic-aware horizontal pod autoscaler (THPA), which operates on top of Kubernetes to enable real-time traffic-aware resource autoscaling for IoT applications in an edge computing environment. THPA performs upscaling and downscaling actions based on network traffic information from nodes to improve the quality of IoT services in the edge computing infrastructure. Experimental results show that Kubernetes with THPA improves the average response time and throughput of IoT applications by approximately 150% compared to Kubernetes with the horizontal pod autoscaler. This indicates that it is important to provide proper resource scaling according to the network traffic distribution to maximize IoT applications performance in an edge computing environment.

Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

As the number of users increased over the years, pioneering technologies and solutions in given areas ceased to be sufficient even in terms of performance. Therefore, there was a need for their development or even redesign and redefinition. One of the issues that undoubtedly has a huge impact on the current shape of the global network and the way information is processed in it is the issue of traffic balancing, especially the one in the server infrastructure related to the WWW service, providing users with the possibility of efficient and reliable web browsing. The paper presents a comparative analysis of selected open-source solutions used for traffic balancing in server infrastructures providing WWW service based on selected criteria. The designed architecture of the test environment and the test results of selected tools implementing the traffic-balancing functionality are presented. Methodologies, test plans, and comparison criteria are proposed. A comparative analysis of results based on specific criteria was performed. The balance between network traffic optimization and load balancing distribution among servers is crucial for the development of energy-efficient data processing centers.

Traffic engineering based on deep reinforcement learning in hybrid IP/SR network

Segment Routing (SR) is a new routing paradigm based on source routing and provide traffic engineering (TE) capabilities in IP network. By extending interior gateway protocol(IGP), SR can be easily applied to IP network. However, upgrading current IP network to a full SR one can be costly and difficult. Hybrid IP/SR network will last for some time. Aiming at the low flexibility problem of static TE policies in the current SR networks, this paper proposes a Deep Reinforcement Learning (DRL) based TE scheme. The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution. As a result, the network congestion can be mitigated and the performance of the network is improved. Simulation results show that our proposed scheme can improve the throughput of the network by up to 9% than existing schemes.