Complex Network Environment Research Articles

A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems

With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by new energy can be injected into the power grid in a stable and safe way, and its power grid adaptability has also received more and more close attention in the field of new energy research. This research focuses on the discussion of PV grid-connected inverters under the complex distribution network environment, introduces in detail the domestic and international standards and requirements on grid-connected inverter grid adaptability, and then analyzes in depth the impacts of the access point voltage changes, access point frequency changes, and access point harmonic changes on the inverters. In order to enhance the adaptability of grid-connected inverters under these abnormal conditions, this research systematically summarizes and concludes a series of inverter adaptive control strategies, which provide literature guidance to effectively reduce the probability of power system faults and improve the reliability of the power system. Finally, the future development direction of PV inverter technology is outlooked, pointing out that, with the increase in the proportion of PV power generation in the power system, PV inverters need to evolve gradually from adapting to the grid to supporting the grid and promote the transformation of PV power generation from the auxiliary power source to the main power source through the integration of PV and energy storage.

Review on Consensus Control of Multi-Agent Systems

This review provides a comprehensive analysis of consensus control strategies for multi-agent systems (MASs) mainly by researching and analysing the control formula designs of different representative MAS. Key approaches are highlighted ,such as finite-time consensus, pinning control, consensus control under DoS attacks, and other advanced control strategies. Each control strategy is discussed in detail regarding its applicability, advantages, and limitations. Furthermore, the strategies ability to address rapid convergence, robustness, and security in complex network environments is emphasized. Besides, several current research trends and key challenges, such as robustness under dynamic uncertainties and resource-constrained communication, are also explored. Future opportunities for enhancing the stability and security of MASs are explained in the final section of this review

SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning

Traffic classification is essential for network management and optimization, enhancing user experience, network performance, and security. However, evolving technologies and complex network environments pose challenges. Recently, researchers have turned to machine learning for traffic classification due to its ability to automatically extract and distinguish traffic features, outperforming traditional methods in handling complex patterns and environmental changes while maintaining high accuracy. Federated learning, a distributed learning approach, enables model training without revealing original data, making it appealing for traffic classification to safeguard user privacy and data security. However, applying it to this task poses two challenges. Firstly, common client devices like routers and switches have limited computing resources, which can hinder efficient training and increase time costs. Secondly, real-world applications often demand personalized models and tasks for clients, posing further complexities. To address these issues, we propose Split Federated Mutual Learning (SFML), an innovative federated learning architecture designed for traffic classification that combines split learning and mutual learning. In SFML, each client maintains two models: a privacy model for the local task and a public model for the global task. These two models learn from each other through knowledge distillation. Furthermore, by leveraging split learning, we offload most of the computational tasks to the server, significantly reducing the computational burden on the client. Experimental results demonstrate that SFML outperforms typical training architectures in terms of convergence speed, model performance, and privacy protection. Not only does SFML improve training efficiency, but it also satisfies the personalized needs of clients and reduces their computational workload and communication overhead, providing users with a superior network experience.

Performance Evaluation of Fault Tolerance in 6G Software Defined Network (SDN)

Traditional Operations and Maintenance (O&M) have made real-time fault location of devices challenging due to the rapid growth of network devices. Therefore, Intelligent O&M in 6G networks leveraged using Software Defined Network (SDN) are expected to optimise network operations through fault tolerance mechanisms. SDN is an emerging paradigm that offers dynamic and efficient solutions to the complex network environment. This paper provides a comprehensive overview of fault tolerance as a critical algorithm for SDN under Intelligent O&M, evaluating the performance of fault tolerance mechanisms and investigating the effectiveness of fault tolerance mechanisms towards TCP, UDP and ICMPv4 with two different scenarios. Simulation results demonstrate that the proposed fault tolerance mechanisms under Intelligent O&M within a 6G network can reduce the delay by 98.79%, ensuring more reliable network infrastructure.

Cloud‐Edge Collaboration‐Based Multi‐Cluster System for Space‐Ground Integrated Network

ABSTRACTAs global informationization deepens, the importance of Space‐Ground Integrated Network (SGIN) as a new network architecture becomes increasingly prominent. SGIN combines the advantages of ground and space networks, enabling global information interconnection and sharing through various communication means such as satellites, drones, and ground stations. However, due to its complex network environment and diverse communication requirements, traditional network architectures struggle to meet its demands for efficiency, stability, and scalability. To address these challenges, we focus on the research, design, and implementation of a cloud‐edge collaboration‐based multi‐cluster system for SGIN. The goal is to construct an efficient, stable, and scalable network system capable of providing seamless global coverage and efficient communication within SGIN. We design a multi‐cluster system architecture based on container technology, leveraging cloud and edge computing techniques for dynamic resource allocation and efficient utilization. This architecture aims to meet the diverse network service requirements of ground terminals, enhancing responsiveness, efficiency, resilience, and reliability of network services. Additionally, we introduce a multipath data transmission mechanism to support the transfer of large‐scale data, such as remote sensing images. A simulation platform tailored for SGIN is developed, demonstrating the feasibility of the multi‐cluster system and the effectiveness of multipath data transmission.

Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments

With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge intelligence. In this context, federated learning, as a new distributed machine learning method, becomes one of the key technologies to realize edge intelligence. Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. However, this fixed infrastructure is difficult to adapt to the complex and ever-changing heterogeneous network environment. With its high degree of flexibility and mobility, the introduction of unmanned aerial vehicles (UAVs) into the federated learning framework can provide enhanced communication, computation, and caching services in edge intelligence networks, but the limited communication bandwidth and unreliable communication environment increase system uncertainty and may lead to a decrease in overall energy efficiency. To address the above problems, this paper designs a UAV-assisted federated learning with a privacy-preserving and efficient data sharing method, Communication-efficient and Privacy-protection for FL (CP-FL). A network-sparsifying pruning training method based on a channel importance mechanism is proposed to transform the pruning training process into a constrained optimization problem. A quantization-aware training method is proposed to automate the learning of quantization bitwidths to improve the adaptability between features and data representation accuracy. In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. Experiments on real-world datasets validate the effectiveness of the scheme. The experimental results show that compared with other federated learning frameworks, the CP-FL approach can effectively mitigate the communication overhead, as well as the computation overhead, and has the same outstanding advantage in terms of the balance between privacy and usability in differential privacy preservation.

MVDet: Encrypted malware traffic detection via multi-view analysis

Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection.

Adaptive Blockchain-Integrated Nonlinear Federated Learning Framework for Real-Time Intrusion Detection in IoT Fog Networks ABFL-RTID

This research presents the Adaptive Blockchain-Integrated Nonlinear Federated Learning (ABFL-RTID) model, designed for real-time intrusion detection in IoT fog networks. The framework leverages nonlinear learning techniques, such as deep neural networks, to enhance detection capabilities in complex and dynamic network environments. Integrating blockchain ensures decentralized security, while federated learning preserves data privacy by enabling local model training on edge devices. The nonlinear models improve adaptability, accurately identifying sophisticated intrusion patterns while securely validating updates through blockchain. Simulations show the framework achieving 96.8% detection accuracy with response times of 150 ms, demonstrating superior scalability and adaptability under changing network conditions. This study provides a practical approach for building resilient and secure intrusion detection systems, enhancing data integrity and privacy without the delays of traditional, centralized models.

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

Research on the Application of AI Technologies in Computer Network Data Analysis

In recent years, with the development of the Internet of Things and smart devices, the Future Internet project has driven innovation in network architecture by combining different models and resources. To manage complexity, creating abstract models has become a critical step in the field of computer networks. This study explores the application of AI techniques in network data analysis, demonstrating the possibilities of AI for real-time monitoring, anomaly detection, and the future development of AI. Despite the promising applications of AI technology, challenges such as privacy protection and dealing with cyber threats still need to be overcome. Future research should develop safer and more flexible AI algorithms to adapt to complex network environments.

Data transmission optimization based on multi-objective deep reinforcement learning

Abstract Simultaneously reducing network energy consumption and delay is a hot topic today. This paper addresses this issue by designing a novel multi-objective data transmission optimization algorithm based on deep reinforcement learning. A three-layer back propagation (BP) neural network is designed to improve the accuracy of environmental state prediction, by learning from historical state and action sequence data, which can help the agent make better decision for routing selection in complex network environment. Based on this, we use Q-Learning to find routing for transmission demands, aggregating more traffic through less links and routers, to reduce energy consumption and delay. To enhance the efficiency and robustness of the algorithm, a new reward mechanism is designed based on the traffic demand and the link state. The algorithm divides candidate links into three levels for path selection so that a better solution can be obtained on the basis of ensuring feasible solutions are obtained. Continuous updating of the Pareto set through multiple state steps approximates the optimal solution. We leverage the Euclidean distance to the reference point to measure the optimization effect of the two objectives. The simulation results show that this algorithm outperforms existing algorithms in reducing energy consumption and network delay.

Survey of federated learning in intrusion detection

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.

VGGIncepNet: Enhancing Network Intrusion Detection and Network Security through Non-Image-to-Image Conversion and Deep Learning

This paper presents an innovative model, VGGIncepNet, which integrates non-image-to-image conversion techniques with deep learning modules, specifically VGG16 and Inception, aiming to enhance performance in network intrusion detection and IoT security analysis. By converting non-image data into image data, the model leverages the powerful feature extraction capabilities of convolutional neural networks, thereby improving the multi-class classification of network attacks. We conducted extensive experiments on the NSL-KDD and CICIoT2023 datasets, and the results demonstrate that VGGIncepNet outperforms existing models, including BERT, DistilBERT, XLNet, and T5, across evaluation metrics such as accuracy, precision, recall, and F1-Score. VGGIncepNet exhibits outstanding classification performance, particularly excelling in precision and F1-Score. The experimental results validate VGGIncepNet’s adaptability and robustness in complex network environments, providing an effective solution for the real-time detection of malicious activities in network systems. This study offers new methods and tools for network security and IoT security analysis, with broad application prospects.

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection

With the rapid development of information technology, network security issues have become increasingly prominent. In particular, data security intrusions pose serious threats to the data privacy and system security of enterprises and individuals. Traditional intrusion detection systems often exhibit low detection accuracy and high false alarm rates when faced with complex and dynamic network environments and diverse attack methods. Therefore, this paper proposes a data security intrusion detection system based on deep learning, which integrates the Mamba model and ECANet model and employs an end-to-end learning approach for training and optimization. First, the Mamba model is introduced for preliminary data feature extraction, whose efficient feature representation capabilities provide a solid foundation for the subsequent detection process. Then, by integrating the ECANet model, feature selection is further optimized through the attention mechanism, enhancing the model’s focus on important features. Finally, an end-to-end learning approach is adopted to train and optimize the entire system, ensuring excellent performance and robustness in practical applications. Experimental results show that the proposed intrusion detection system demonstrates higher detection accuracy on multiple test datasets, improving by approximately 5% compared to traditional methods, providing a new and effective solution for data security.

Research on rumor propagation and rumor refutation strategies in complex network environment

Research on rumor propagation and rumor refutation strategies in complex network environment

Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.

Effective Intrusion Detection through Hybrid CNN-LSTM and Grey Wolf Optimization for Feature Selection in Complex Network Environments

Security and threat identification have always been critical in the interconnected world we live in today, and with emerging and cutting-edge threats, they still remain vital. Consequently, this paper proposes an intrusion detection solution whereby GWO for feature selection to optimize the detection model and a CNN–LSTM for attack detection. This paper describes the potential threat attributed to high dimensionality and non-linearity of network traffic data and aims to improve the overall detection rate and accuracy of different types of cyber-attacks. The GWO algorithm efficiently addresses the selection of relevant features from the data, hence demystifying the data while enhancing the model’s impact. CNN-LSTM architecture adapted from convolution neural networks and long short-term memory nets which performed the best in feature recognizing on networks traffic, allows the detection model to get understanding about complicated patterns in traffic. The described approach is compared with common methods, including Logistic Regression, Gaussian Naive Bayes, Decision Tree, and Random Forest and outperforms each of those regarding precision, recall and F1-score. Evaluation of the results obtained indicates that the proposed system correctly identifies various types of attacks with high precision, and very sensitive to false positive, and scenes the DoS/DDoS, PortScan, BruteForce, Web Attacks, and Botnet attacks correctly and distinctly. This paper shows the efficiency of integrating the state-of-art optimization methods with DL-based models for the development of the sound and highly scalable intrusion detection framework. The proposed method provides a much higher level of security enhancement in the proposed network against the emerging threat of cyber-attacks on existing and new complex networks.

PRP protocol optimization method based on Q-learning under the Linux operating system

Abstract In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. The experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability.

A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder

Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide secure operation in complex and changing network environments. To avoid information loss caused when handling traffic data while improving the detection performance of traffic feature information, this paper proposes a multi-information fusion model based on a convolutional neural network and AutoEncoder. The model uses a convolutional neural network to extract features directly from the raw traffic data, and a AutoEncoder to encode the statistical features extracted from the raw traffic data, which are used to supplement the information loss due to cropping. These two features are combined to form a new integrated feature for network traffic, which has the load information from the original traffic data and the global information of the original traffic data obtained from the statistical features, thus providing a complete representation of the information contained in the network traffic and improving the detection performance of the model. The experiments show that the classification accuracy of network traffic anomaly detection using this model outperforms that of classical machine learning methods.