Real-time Network Research Articles

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Cyber-attacks are becoming increasingly common and sophisticated, and the security of net-works is a pressing concern for companies everywhere. To spot and stop attempts to access network resources without authorization, intrusion. There is, however, a problem that many traditional IDS methods can generate a lot of false alarms. This means that it misidentifies safe activity as a threat and is not readily accepted by security teams for which real issues. Scalability and resistance to evolving threats a hybrid approach of a neural network combined with an adaptive algorithm was used in this paper focuses on improvement the performance and accuracy of the intrusion detection system proposed by us. Combining genetic algorithms and particle swarm optimization with neural networks using a framework optimized for comprehensive performance is present in this tutorial. More to the point, parameters and training procedures for Deep learning models in intrusion detection tasks. The objective of this research paper model is to make a false alarm the rate should be decreased, search accuracy increased and in real-time network environments, the system should adapt easily. Experimental results show that the combination of optimization algorithms and neural networks outperforms traditional IDS methods.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation

Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology.

Retraction Note: Real-time network virtualization based on SDN and Docker container

Retraction Note: Real-time network virtualization based on SDN and Docker container

Securing Real-Time Payment Systems: Challenges and Solutions for Network Security in Banking

Modern banking infrastructure relies heavily on real-time payment technologies, which have revolutionized how financial institutions handle transactions worldwide. This in-depth article examines the operational needs, security procedures, and technical difficulties of setting up and managing real-time payment networks. The article examines advanced fraud prevention techniques, such as rule-based systems and machine learning applications, while examining the crucial trade-off between transaction speed and security. This article illustrates the development of payment infrastructure and security measures through in-depth case studies of European and Asian payment networks. To help financial institutions, regulators, and technology suppliers navigate this quickly changing industry, the essay delves deeper into new technologies, regulatory factors, and upcoming advancements influencing the real-time payments market.

Enhanced Swine Behavior Detection with YOLOs and a Mixed Efficient Layer Aggregation Network in Real Time.

Effective livestock management has become essential owing to an aging workforce and the growth of large-scale farming operations in the agricultural industry. Conventional monitoring methods, primarily reliant on manual observation, are increasingly reaching their limits, necessitating the development of innovative automated solutions. This study developed a system, termed mixed-ELAN, for real-time sow and piglet behavior detection using an extended ELAN architecture with diverse kernel sizes. The standard convolution operations within the ELAN framework were replaced with MixConv using diverse kernel sizes to enhance feature learning capabilities. To ensure high reliability, a performance evaluation of all techniques was conducted using a k-fold cross-validation (k = 3). The proposed architecture was applied to YOLOv7 and YOLOv9, yielding improvements of 1.5% and 2%, with mean average precision scores of 0.805 and 0.796, respectively, compared with the original models. Both models demonstrated significant performance improvements in detecting behaviors critical for piglet growth and survival, such as crushing and lying down, highlighting the effectiveness of the proposed architecture. These advances highlight the potential of AI and computer vision in agriculture, as well as the system's benefits for improving animal welfare and farm management efficiency. The proposed architecture enhances the real-time monitoring and understanding of livestock behavior, establishing improved benchmarks for smart farming technologies and enabling further innovation in livestock management.

Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.

DESENet: a bilateral network with detail-enhanced semantic encoder for real-time semantic segmentation

Abstract Real-time semantic segmentation is widely used in various domains such as autonomous driving. Most real-time semantic segmentation networks adopt an encoder-decoder structure. During encoding, feature maps undergo multiple downsampling stages to enhance computational efficiency and enlarge the receptive field. However, this process may lead to the loss of detailed information, which may not be fully recovered during upsampling. Moreover, upsampling semantic features may cause blurring due to the lack of spatial detail information. To mitigate these issues, we use spatially guided cross-resolution self-attention to improve the upsampling of the semantic features by supplementing detailed information from the spatial branch. Furthermore, we incorporate an inductive bias into the cross-resolution attention mechanism to enhance its ability to learn generalized features. Additionally, we design a semantic feature extraction block, and a spatial feature extraction branch to construct a lightweight backbone. The results on Cityscapes and CamVid show that the proposed model achieves a good balance between accuracy and efficiency. Specifically, it obtains 74.2 and 71.5 mIoU on the two test datasets with 1.31M parameters, respectively. Code is available on https://github.com/clearwater753/DECENet.

Exploring hybrid models for identifying locations for active mobility pathways using real-time spatial Delphi and GANs

The spatial planning process is considered an extremely complex system, as it comprises different variables that interrelate and interact with each other. Effectively addressing this spatial complexity necessitates a multidisciplinary approach, as unified methodologies may prove insufficient. Specifically, in urban planning, it is increasingly crucial to prioritize bike lanes, bike stations, and pedestrian zones, for functional transportation infrastructures. This approach can enhance cities by improving air quality, reducing emissions, and boosting public health and safety through physical activity and accident prevention. However, implementing these changes requires careful planning, community engagement, and stakeholder collaboration. This paper proposes a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones adopting Real-Time Spatial Delphi and Generative Adversarial Networks (GANs). The Real-Time Spatial Delphi is a modified version of the traditional Delphi method that incorporates real-time feedback and visualization of group response in real-time, aiming to achieve a convergence of opinions among experts on the territory. Nevertheless, these judgments are a spatial representation not visible in reality, and with the spread of artificial intelligence models, different implementations can support the planning process, such as the use of GANs. In this case, GANs can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin. The results showcased how the method helps stakeholders, policymakers, and citizens in visualizing the proposed changes and gauging their potential impact with greater precision.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Strengthening medical facility responses to respiratory infectious diseases: Global trends, challenges, and innovations post-COVID-19.

Respiratory infectious diseases have long been a serious public health problem. This study explores the significance of respiratory infectious disease prevention and control systems worldwide, particularly during and after the COVID-19 pandemic. The epidemiology of many respiratory diseases such as influenza changed over the past two years, and the incidence of pathogens such as Mycoplasma pneumoniae and Bordetella pertussis has also increased. Based on influenza surveillance data in China, the influenza season in 2023 was notably delayed and extended, with A(H1N1) pdm09 being the predominant strain. This editorial also reviewed the gradual establishment of China's infectious disease prevention and control system following the 2003 SARS outbreak, highlighting the role of medical facilities in managing public health emergencies, conducting infectious disease pre-screening, and reporting cases to networks in real time. In the future, China will further develop an intelligent, multi-trigger infectious disease surveillance and early warning system to increase the early detection of unknown infectious diseases and optimize the allocation of medical resources. A robust infectious disease control system is crucial to addressing future pandemic threats.

Neural Transformers for Zero-Day Threat Detection in Real-Time Cybersecurity Network Traffic Analysis

Neural Transformers for Zero-Day Threat Detection in Real-Time Cybersecurity Network Traffic Analysis

Deriving water quality index using site-specific water quality parameter guidelines for real-time water quality stations

ABSTRACT The Newfoundland and Labrador real-time water quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near real time. The network generates vast amounts of data that are too complex for many stakeholders to interpret. While the Canadian Council of Ministers of the Environment water quality index (WQI) has been a valuable tool to simplify and summarize the ambient quality of water, its usage in RTWQ is virtually non-existent. This is largely due to the lack of alignment/congruency between the limited number of parameters from real-time networks and the availability of aquatic water quality guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site-specific water quality guidelines using the background concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine various influences on the scores, including precipitation. The influence of each parameter on the WQI score is also plotted in the computation of the RTWQ WQI. Comparison is made between the yearly scores of the Canadian Environmental Sustainability Indicators WQI and RTWQ WQI. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.

Lightweight and efficient deep learning models for fruit detection in orchards

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.3 \ imes 10^5$$\\end{document}), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

Slice admission control in 5G wireless communication with multi-dimensional state space and distributed action space: A sequential twin actor-critic approach

Network slicing represents a paradigm shift in the way resources are allocated for different 5G network functions through network function virtualization. This innovation aims to facilitate logical resource allocation, accommodating the anticipated surge in network resource requirements. This will harness automatic processing, scheduling, and orchestration for efficient management. To overcome the challenge of managing network resources under heavy demand, slice providers need to leverage both artificial intelligence and slice admission control strategies. While 5G network resources can be allocated to maintain a slice, the logical allocation and real-time network evaluation must be continuously examined and adjusted if network resilience is to be maintained. The complex task of leveraging slice admission control to maintain 5G network resilience has not been fully investigated. To tackle this problem, we propose a machine learning approach for slice admission control and resource allocation optimization so as to maintain network resilience. Machine learning algorithms offer a powerful tool for making robust and autonomous decisions, which are crucial for effective slice admission control. By intelligently allocating resources based on real-time demand and network conditions, these algorithms can help ensure long-term network resilience and achieve key objectives. While various machine learning algorithms hold promise for 5G resource management and admission control, reinforcement learning (RL) has emerged as a particularly exciting solution. Its ability to mimic human learning processes makes it a versatile solution, well-suited to tackle the complex challenges of network control. To fill this gap, we propose a new technique known as sequential twin actor critic (STAC). Simulations show that the STAC improves network resilience through enhanced admission probability and overall utility.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.