Communication Network Environment Research Articles

Adaptive fault‐tolerant platooning control for nonlinear homogeneous multiple autonomous vehicles under unreliable communication networks

AbstractThis article presents a fault‐tolerant platooning control algorithm for a class of nonlinear homogeneous multi‐vehicle systems under unreliable network communication environment. The considered types of actuator failures in individual vehicle are loss‐in‐effectiveness, and bias faults. Suppose the communication network among vehicles is here described as a switching graph subject to intermittent communications. Unlike the existing results dealing with vehicle platoon under continuous communications, this article develops a hierarchical fault‐tolerant platooning control approach, under which, the multiple vehicle coordination platooning objective is ensured even in the cases of discontinuous communication faults and actuator failures. First, each following vehicle can estimate the desire reference information via cooperative adaptive virtual estimator even in intermittent communications. Second, the local fault‐tolerant controller with adaptive parameter update mechanism can guarantee the practical position and velocity of following vehicles asymptotically track the desire estimations. Finally, a simulation example of a team of autonomous vehicles is given to verify the effectiveness of the proposed method.

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Identifying and preventing malicious network behavior is a challenge for establishing a secure network communication environment or system. Malicious activities in a network system can seriously threaten users’ privacy and potentially jeopardize the entire network infrastructure and functions. Furthermore, cyber-attacks have grown in complexity and number due to the ever-evolving digital landscape of computer and network devices in recent years. Analyzing network traffic using network intrusion detection systems (NIDSs) has become an integral security measure in modern networks to identify malicious and suspicious activities. However, most intrusion detection datasets contain imbalance classes, making it difficult for most existing classifiers to achieve good performance. In this paper, we propose a double-layer feature extraction and feature fusion technique (CNN-GRU-FF), which uses a modified focal loss function instead of the traditional cross-entropy to handle the class imbalance problem in the IDS datasets. We use the NSL-KDD and UNSW-NB15 datasets to evaluate the effectiveness of the proposed model. From the research findings, it is evident our CNN-GRU-FF method obtains a detection rate of 98.22% and 99.68% using the UNSW-NB15 and NSL-KDD datasets, respectively while maintaining low false alarm rates on both datasets. We compared the proposed model’s performance with seven baseline algorithms and other published methods in literature. It is evident from the performance results that our proposed method outperforms the state-of-the-art network intrusion detection methods.

A novel method for effective intrusion detection based on convolutional speaking neural networks

These days, most people can't imagine their life without their networks. People rely more and more on networks as new technologies like 5G and the Internet of Things proliferate, and networks are becoming larger and more complicated by the day. Consequently, the enormous networked society is under increasing danger from cyber attacks, which are also growing in number, sophistication, and variety. Users' private data can end up in the wrong hands. Data communicated via a network might be compromised in some way. It is also possible to target the computer infrastructure that is linked to the network. Thus, NIDS is crucial in ensuring that contemporary society's network communication environment is secure and dependable. This work proposes a detection model that is both lightweight and effective. This model is able to keep its high detection accuracy by combining the benefits of SNN and CNN with logical algorithm design. On top of that, it can bring attention to clear strengths in relation to computing efficiency and power usage. The suggested model was tested against a number of state-of-the-art models using an extensive and integrated set of benchmarks. With a 23 % improvement in detection accuracy (AD), a 19 % improvement in delay, and a 28 % improvement in consumption energy, the suggested technique outperformed the Spiking DFR + MLP and Spike-DHS methods in the simulations.

Retracted: High-Concurrency Big Data Precision Marketing and Advertising Recommendation under 5G Wireless Communication Network Environment

Retracted: High-Concurrency Big Data Precision Marketing and Advertising Recommendation under 5G Wireless Communication Network Environment

Improvement of sampled-data-based stabilization and dissipativity conditions for T–S fuzzy systems under network communication environment

This paper aims to develop an improved sampled-data controller for T–S fuzzy systems in the presence of external disturbances and network-induced delays. To take more advantage of information about system state responses excited during sampling and data transmission, this paper introduces novel time integrals of the weighted derivative of the system state and they are utilized to construct an improved looped functional. Additionally, a refined discontinuous function is incorporated into the Lyapunov–Krasovskii functional, and three additional zero-equalities are incorporated into the stabilization conditions to enhance the relationship between the time integrals and other components of the augmented state. Moreover, this paper devises a less conservative relaxation technique that can numerically solve the dissipativity-based stabilization conditions dependent on affine-matched fuzzy-basis functions by exploiting their strict constraints. Finally, two examples are presented to illustrate the validity and applicability of the proposed approach.

Retracted: Comprehensive Evaluation of Teaching Ability Based on Network Communication Environment

Retracted: Comprehensive Evaluation of Teaching Ability Based on Network Communication Environment

Peer-to-Peer Federated Learning for COVID-19 Detection Using Transformers

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively.

LSTM과 GRU를 활용한 업종별 전력사용량 데이터 보간 방법

The electricity consumption data is collected using the communication network from the smart meter. Missing values occur depending on the status of the meter and the communication network environment. In previous studies, missing value interpolation was suggested as a method of using electricity users

Analysis of Network Security Countermeasures From the Perspective of Improved FS Algorithm and ICT Convergence

In this paper, the forward selection (FS) algorithm is introduced on the basis of information and communication technology, and the design of intrusion detection method for communication network is carried out. By studying the classification and detection pattern matching of communication network intrusion behavior, extracting the intrusion behavior features of communication network based on FS algorithm, and optimizing the intrusion detection and learning effect based on the limit learning machine, the intrusion behavior attributes of communication network are clarified, and a new detection method is proposed to solve the problems of low detection accuracy and low recall in the current intrusion behavior detection of complex communication network environments. Compared with the intrusion detection method based on GA-SVM algorithm, the accuracy of the detection results reaches 94.23%, and the recall rate exceeds 97%, which is obviously better than the 85% accuracy and 75% recall rate of the traditional detection method, which can ensure the security of the communication network environment. In addition, this paper proposes the APDR dynamic comprehensive information security assurance system model, which has considerable flexibility and can respond to current network security requirements.

Retracted: College Sports Multimedia Network Based on Wireless Communication and Deep Learning Network Environment

Retracted: College Sports Multimedia Network Based on Wireless Communication and Deep Learning Network Environment

A Deep Learning-Based Routing Approach for Wireless Mesh Backbone Networks

Optimal routing decisions are key in communication network environments to minimize bottlenecks such as traffic congestion and limited bandwidth. Routing in wireless mesh backbone networks is the focus of this study given that they are popular particularly for providing broadband connectivity to a huge number of users accessing and transmitting multimedia data hence are susceptible to communication-oriented bottlenecks. Existing routing solutions are mainly optimistic approaches. These mainly depend on link states, distance and hop counts which present a routing generalization bottleneck especially in huge wireless mesh networks because it is difficult to get the entire footprint of the network. Simply put, it is very difficult to determine an optimal route in a huge wireless mesh network (WMN) with dynamic network conditions. Since deep learning has a strong generalization ability. In this paper, a deep learning-based routing approach is proposed with the goal of ensuring a defined optimal quality of service (QoS) in a WMN. In order to achieve the study purpose, a wireless mesh network simulation environment is built and a network data feature set is orchestrated to generate a data set used to train a Long short Term Memory (LSTM)-based deep learning model that estimates the route with the optimal QoS. The generated data set is validated by training other learning models including the Multilayer Perceptron (MLP), Logistic Regression (LR) and Random Forest (RF). Our results show that the routes selected by the LSTM-based model provides the best packet delivery ratio (PDR) and throughput. Our results further show that the learning models (MLP, LR, RF) also provide better PDR and throughput compared to the traditional Ad-hoc On-demand Distance Vector (AODV) routing protocol.

A Fractional-Order SIR-C Cyber Rumor Propagation Prediction Model with a Clarification Mechanism

As communication continues to develop, the high freedom and low cost of the communication network environment also make rumors spread more rapidly. If rumors are not clarified and controlled in time, it is very easy to trigger mass panic and undermine social stability. Therefore, it is important to establish an efficient model for rumor propagation. In this paper, the impact of rumor clarifiers on the spread of rumors is considered and fractional order differentiation is introduced to solve the problem that traditional models do not take into account the “anomalous propagation” characteristics of information. A fractional-order Susceptible-Infected-Removal-Clarify (SIR-C) rumor propagation prediction model featuring the clarification mechanism is proposed. The existence and asymptotic stability conditions of the rumor-free equilibrium point (RFEP) E0; the boundary equilibrium points (BEPs) E1 and E2 are also given. Finally, the stability conditions and practical cases are verified by numerical simulations. The experimental results confirm the analysis of the theoretical study and the model fits well with the real-world case data with just minor deviations. As a result, the model can play a positive and effective role in rumor propagation prediction.

Finite‐time secure state estimation for a class of switched systems subject to deception attacks

AbstractThis article is concerned with the finite‐time secure state estimation problem for switched systems subject to malicious attacks. The sensor data is considered transmissible via a non‐secure communication network environment, where the communication channel might be subject to deception attacks. By dividing the sensor data that is subject to attacks into two parts, related and unrelated to the original measurements, a switched secure state estimation error system model is established. Moreover, based on the average dwell time method, sufficient conditions are derived for the switched secure state estimation error system to be finite‐time stable, as well as finite‐time boundedness. An existence condition of the finite‐time secure state estimator gains with a desired performance is given. Subsequently, the finite‐time secure state estimator gains' design is transformed to solve a set of linear matrix inequalities. Finally, numerical examples are used in order to show the effectiveness of the proposed finite‐time secure state estimator design method.

Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms

The terminal equipment interconnection and the network communication environment are complex in power cyber–physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an active defense framework against FDIAs of power CPS based on data-driven algorithms in order to ensure that FDIAs can be efficiently detected and processed in real-time during power grid operation. First, the data transmission scenario and false data injection forms of power CPS were analyzed, and the FDIA mathematical model was expounded. Then, from a data-driven perspective, the algorithm improvement and process design were carried out for the three key links of data enhancement, attack detection, and data reconstruction. Finally, an active defense framework against FDIAs was proposed. The example analysis verified that the method proposed in this paper could effectively detect FDIAs and perform data reconstruction, providing a new idea for the active defense against FDIAs of power CPS.

Balanced Scheduling Method of Network Information Resources for Cloud Storage

In the network communication environment, the balanced scheduling of network information sharing resources can better improve the service quality of network communication. In the current network communication sharing resource balanced scheduling process, there are some problems, such as the completion time of resource scheduling is too long, the cost consumption is large, and so on. This paper proposes a balanced scheduling method of network information resources for cloud storage. By constructing the storage model of network communication shared resources, the network communication structure under big data is layered. Through the characteristic analysis of shared resource, equilibrium ratio is introduced to obtain the fitness function of network communication shared resources. The fitness function and convergence factor are used to solve the problem. The experimental results show that the network information resource balanced scheduling method for cloud storage needs shorter completion time and lower cost, which fully meets the research requirements.

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

At present, the application of information technology has realized the transformation of people’s production and lifestyle, and it has also promoted the transformation of the sports industry. At present, the application of informatization in the basketball field in the sports industry is increasing. By using the advanced methods and technologies of its information display, this article aims at realizing the optimization of the basketball recognition pattern in the mobile network communication environment to promote the ecological development of the sports industry. It proposes to use panoramic vision to guide the integration of computer network and basketball field, which is helpful to analyze and solve the objectivity problems of single action and system incompatibility in basketball recognition simulation. By referring to the principles and laws of panoramic vision, the identification module and simulation module of the basketball auxiliary training system are constructed and optimized, so as to realize the promotion of basketball and the ecologicalization of teaching. In the research on the basketball recognition simulation system, the three-dimensional skeleton key point coordinate data are input into the ST-GCN network for comparison and testing. Specifically, it includes 2D key point coordinate data and 3D key point coordinate data as input to conduct model research and testing in the self-made basketball dataset. The experimental results show that the recognition rates of 2D and 3D coordinates are 66.64% and 87.69%, respectively. Therefore, it is crucial to use the human panoramic vision algorithm to convert 2D coordinates to 3D coordinates.

Survey of Secure Communications of Internet of Things with Artificial Intelligence

The Internet of Things(IoT) with artificial intelligence aims to build a network that enables smart devices to communicate autonomously and efficiently. It is one of the fastest-growing fields in the information era. It encompasses billions of smart devices worldwide. With the explosive growth of the number of Internet of Things access devices, the types of access network devices are also complex and diverse, especially the heterogeneity of the network is becoming more and more prominent. Faced with such a huge and complex network architecture and communication environment, the existing network security architecture is facing huge challenges. The paper first analyzes the existing three-tier architecture of the IoT and possible security issues, including DDoS Attack, Replay Attack, SQL Injection, Man-in-The-Middle Attacks, and illegal Access. Internet security solutions, including Security Framework, Security Routing Protocol, Authentication Protocol, Data Encryption Transfer & Storage, Key Exchange Protocol and Intrusion Detection, and then explore the possible applications of quantum computing and quantum communication in IoT security, including Quantum Security Hardware Used in IoT, Quantum Key Distribution (QKD) in IoT, Intrusion Detection During QKD with Deep Learning (DL) and Data Encryption Protocol in IoT Based On Quantum Walks (QW), and finally discussed the opportunities and trends of existing security solutions for the IoT Challenges, these challenges and trends can be used as potential research directions for future IoT security.

Network Amplification of Politicized Information and Misinformation about COVID-19 by Conservative Media and Partisan Influencers on Twitter

ABSTRACT Social media amplification is both a mechanism to attract public attention and a process of information diffusion shaped by the online social network structure. This study focuses on amplification by elites on social media and examines the extent to which traditional media and emerging partisan influencers engage in “network amplification.” Defined as like-minded elites sharing similar or/and mutual messages, network amplification highlights the interrelation and interaction between elite messages in the network communication environment of social media. This is a phenomenon worth investigating because network amplification’s resulting message repetition and reinforcement can multiply the overall effectiveness of elite messaging. Using network sampling and spectral clustering, we collected 358,707 accounts that followed 2,069,311 accounts on Twitter and detected nine distinct networks of traditional media and emerging partisan influencers. We then examined their 3,540,629 tweets related to the COVID-19 pandemic. Results show that 1) conservative media and influencers engaged in network amplification of politicized information and misinformation significantly more than liberal media and influencers did; 2) conservative influencers exhibited a stronger tendency to retweet and align their messages with conservative media than liberal influencers did regarding liberal media; and 3) traditional media partially drove partisan influencers’ amplification. The implications of network amplification for partisan asymmetry, misinformation, and public opinion are discussed.

High-Concurrency Big Data Precision Marketing and Advertising Recommendation under 5G Wireless Communication Network Environment

With the rise of 5G wireless communication networks, information technology has challenged the traditional practice of marketing. The impact of enterprise precision marketing theory on traditional marketing theory was brought by massive user data in the era of big data. With the sudden rise and rapid development of mobile Internet and its derived big data, in the fierce market competition of new media and we-media, how to provide accurate information push for users has become the primary subject of research. The design of advertising precision marketing system mainly uses 5G network as the basic tool and makes precise positioning of consumer demand through high-concurrency big data analysis system. K -means clustering algorithm improves the data analysis system to provide personalized demand customization and deeply excavates the segmentation of the consumer market and the real demand of consumers. The results show that the model effectively enhances its adaptability. When S value is between 0.01 and 0.05, the variable has a high significance, and precision marketing improves the accuracy of recommendation.

Tripartite Quantum Key Distribution Implemented with Imperfect Sources

Multipartite quantum key distribution (QKD) is a promising area of quantum networks that provides unconditional secret keys among multiple parties, enabling only legitimate users to decrypt the encrypted message. However, security proofs of existing multipartite QKD typically assume perfect state preparation devices of legitimate users and neglect the relative rotation of reference frames. These presumptions are, nevertheless, very difficult to meet in practice, and thus the security of current multipartite QKD implementations is not guaranteed. By combining the idea of a loss tolerant technique, introduced by Tamaki et al. (K. Tamaki et al., Phys. Rev. A, 90, 052314, 2014), and the concept of a reference frame-independent protocol, we propose a three-party QKD protocol that considers state preparation flaws and the slow drift of reference frames. Through a numerical simulation, the influence of misaliged reference frames on the protocol’s stability was examined by drifting reference frames through angles β=π/5, β=π/6 and β=π/7. In addition, the performance of the proposed protocol was examined for the encoding flaws set at δ=0.35, δ=0.20, and δ=0.10. The results show that the protocol is robust against state preparation flaws, and is insignificantly impacted by misalignment of the reference frames because the achieved transmission distances and secret key rates are comparable to the perfect scenarios. This work dramatically contributes toward the realization of practical and secure multipartite QKD. The proposed protocol has direct applications in quantum communication network environments that involve unknown and slowly varying reference frames, web conferences, and online communications.