Computer Networks Research Articles

Navigating Networks of Suspect Speech: The Abolitionist Mail Controversy of 1835

This article conducts a historical analysis of newspaper coverage and governmental debates from 1834 to 1837 over whether abolitionists should be restricted from the postal networks in the South. Three thematic categories emerged from my analysis of a controversy over abolitionists using the postal network in 1835: (1) balancing government regulation with free expression in the postal network, (2) concerns about individual postmasters meddling in the network, and (3) support for regulation, bridging postal law reform and ethics. This article contends that the 1835 debate showed the limitations of a libertarian understanding of free expression in the United States. These findings contextualize contemporary debates over reconciling the First Amendment with government regulation of digital networks when fashioning media policies that shape content moderation and free expression on digital platforms.

Research on classified prediction algorithm of platform customer value based on "tree" concept

The concept of "trees," representing decision trees, random forests, and XGBoost algorithms, has gained increasing attention in the field of classification prediction in recent years. These tree-based machine learning algorithms have been widely applied in the e-commerce sector. This paper discusses the decision tree, random forest, and XGBoost algorithms individually, and extracts useful classification rules from large volumes of online customer data to provide intelligent decision support for computer network customer management. The study finds that algorithms related to the "tree" concept can integrate a customers current value (such as purchasing behavior) with potential value (such as customer interest inferred from online reviews) through detailed classification criteria, thereby constructing a customer value measurement model. The evolution from decision trees to random forest and XGBoost algorithms has effectively promoted research on customer value hierarchy prediction, improving the efficiency of data mining in computer networks.

Barriers and facilitators to bridging the healthcare digital divide for the older adults: A qualitative research from patients in China.

The aim of this study was to explore the barriers and facilitators underlying the healthcare digital divide by using qualitative methodologies, integrated with the Capabilities, Opportunities, Motivations, and Behaviors (COM-B) as well as the Behavior Change Wheel (BCW) theoretical frameworks. To conduct a thorough analysis, semi-structured，in-depth interviews were orchestrated among a cohort of older patients who had experiences with digital healthcare systems. Data were meticulously analyzed via Colaizzi's seven-step methodological procedure. Nine barriers were elucidated, including physical limitation, inadequate training and support, self-imposed denial, failure to rebuild the digital healthcare environment, equipment constraints, deficits in communication and feedback, skepticism regarding data accuracy and validity, sense of social deprivation, and network information boycott. Conversely, eleven facilitators were identified, encompassing peer modeling, availability of training and support, reconstructive efforts toward the digital healthcare environment, mandatory usage, familial influence, media impact, scientific publicity and education, enhancement of self-efficacy, sense of social participation, perceived convenience, and access to healthful information. Based on the COM-B and BCW models, healthcare organizations may be able to improve their digital healthcare support systems (capabilities), technical design, network speed, and equipment configuration (opportunities), as well as focus on "human-computer integration," the parallel existence of digital technology and humanistic care, and taking into account the issues of information security, accuracy, and human rights equality (motivation) while benefiting the patients, in order to bridge the healthcare digital divide for the older adults.

Explore the Evolution of Computer Network Architecture

Since the invention of computer networks in the 1960s, their architecture has undergone many major innovations and evolutions. This review aims to sort out and analyze this evolution process and reveal its internal motivations and fundamental driving forces. We will analyze the development of network architecture from multiple dimensions such as network topology, protocol stack, and control mechanism, and reveal the internal logic of the architecture evolving with the times.First, the paper reviews the origins of computer network development, explains the original packet switching ideas and ARPANET architecture, and analyzes its influence on later generations. Then it focuses on the birth of the TCP/IP protocol family and its architecture, analyzing its design concepts and innovations. Subsequently, the paper will explore the rise of the Internet and the formation of its layered architecture model, commenting on the advantages and disadvantages of this model. We will also examine the ideas and attempts of the next generation Internet architecture, such as active networks and content-centric networks, and analyze their challenges and limitations.In addition, the paper will explore the new changes brought about by emerging network paradigms such as software-defined networking (SDN) and network function virtualization (NFV) to network architecture, and comment on their advantages and impacts. We will also focus on the driving role of emerging paradigms such as cloud computing and edge computing on network architecture. Finally, the paper will look forward to new trends and challenges in the development of future network architecture, such as the potential application prospects of cutting-edge technologies such as artificial intelligence, machine learning, and blockchain in network architecture. By comprehensively analyzing and summarizing the evolution of network architecture, the paper hopes to provide readers with a macro perspective and a deep understanding of the internal driving force and development logic of network architecture innovation. This will not only help us understand the past, but also provide inspiration and ideas for future network architecture innovation.

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

True Random Number Generator Based on Chaotic Oscillation of a Tunable Double-Well MEMS Resonator.

Chaotic systems have aroused interest across various scientific disciplines such as physics, biology, chemistry, and meteorology. The deterministic but unpredictable nature of a chaotic system is an ideal feature for random number generation. Microelectromechanical systems (MEMS) are a promising technology that effectively harnesses chaos, offering advantages such as a compact footprint, scalability, and low power consumption. This paper presents a true random number generator (TRNG) based on a double-well MEMS resonator integrated with an actuator and position sensor. The potential energy landscape of the proposed MEMS resonator is actively tunable with a direct current voltage. Experimental demonstrations of tunable bistability and chaotic resonance are reported in this paper. A chaotic time sequence is generated through piezoresistive sensing of the position of the MEMS resonator once it is driven into the chaotic regime. Subsequently, the randomness of the bit sequence, achieved by applying the exclusive or function to a digital chaotic sequence and its delayed differential is confirmed to meet the National Institute of Standards and Technology specifications. Moreover, the throughput and energy efficiency of the proposed MEMS-based TRNG can be adjusted from 50 kb s-1 and 0.44 pJ per bit at a low energy barrier to 167 kb s-1 and 6.74 pJ per bit at a high energy barrier by changing the MEMS device's potential well. The tunability of the proposed double-well MEMS resonator not only offers continuous adjustments in the energy efficiency of TNRG but also unveils vast and diverse research opportunities in analog computing, encryption, and secure communications.

A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC

Due to the limited payload and power of drones, the computational overhead, storage overhead and communication overhead that can be used for secure communication are restricted, making it difficult to apply some complex but fairly secure authentication protocols on drones. In this paper, we propose a low-complexity protocol for storing identity information in a resource-unconstrained device that does not require the UAV to store the information, thereby enhancing the UAV’s resistance to capture. The protocol in this paper mainly consists of quasi-cyclic low-density parity-check (QC-LDPC) codes, physical unclonable functions (PUFs) based on random-access memory (RAM), “XOR” operations, and hash computation. The protocol in this paper is an authentication architecture in which the drone is guided by the ground station to read its identity information, and the drone does not store any identity information in advance. The protocol is divided into two phases: 1. fuzzy authentication of fingerprint PUF and 2. uniqueness authentication accomplished while guiding the recovery of identity PUF. Recovering identity PUF in this paper, QC-LDPC is used as the error control module, and the optimization of bit-flip decoding significantly reduces the probability of decoding failure. After the comparative security analysis and comparative overhead analysis of this paper’s protocol, it can be concluded that this paper’s protocol can withstand common attacks (including attacks attempting to pass authentication, attacks attempting to interfere with authentication, and physical capture attacks), and the storage and communication overhead is small in the case of large time overhead.

Carbon Emission Effects of Spatially Connected Networks in the Digital Economy： Dynamic Evolution and Mechanism of Action

In the context of the "double carbon" goal and the construction of the "East Counts, West Counts" project, this study investigated the characteristics of the spatial and temporal evolution of the digital economy spatial correlation network, and the effects and mechanisms of carbon emission reduction and carbon rebound effects by selecting data at the level of 254 prefectural-level cities from 2011 to 2021 and using the UCINET software and empirical econometric modeling. The results indicated that： The spatial network of the digital economy represented the development pattern of multiple synergies and had the characteristics of non-equilibrium spatial evolution in geographic regions. Secondly, the spatial network of the digital economy in the sample selection period had a positive contribution to carbon emission reduction, and it led to the carbon rebound effect, which was still valid after the robustness test. Furthermore, the spatial correlation network of the digital economy could reduce carbon emissions by promoting the energy efficiency effect under technological progress and led to the carbon rebound effect. Finally, the empirical results were heterogeneous under the four dimensions of geographic region, administrative level, urban environmental governance objectives, and resource endowment.

Privacy-Preserving Authentication Based on PUF for VANETs

The secret key is stored in an ideal tamper-proof device so that a vehicle can implement a secure authentication with the road-side units (RSUs) and other drivers. However, some adversaries can capture the secret key by physical attacks. To resist physical attacks, we propose a physical-preserving authentication based on a physical unclonable function for vehicular ad hoc networks. In the proposed scheme, a physical unclonable function is deployed on the vehicle and the RSU to provide a challenge–response mechanism. A secret key is only generated by the challenge–response mechanism when it is needed, which eliminates the need to store a long-term secret key. As a result, this prevents secret keys from being captured by adversaries, improving system security. In addition, route planning is introduced into the proposed scheme so that a vehicle can obtain the authentication key of RSUs on its route before vehicle-to-infrastructure authentication, which greatly speeds up the authentication when the vehicle enters the RSUs’ coverage. Furthermore, a detailed analysis demonstrates that the proposed scheme achieves security objectives in vehicular ad hoc networks. Ultimately, when contrasted with similar schemes, the performance assessment demonstrates that our proposed scheme surpasses others in terms of computational overhead, communication overhead and packet loss rate.

An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

A Research on Consumer views of the Impacts of Electronical Commerce

Commerce is a new term that refers to the practice of buying, selling, or exchanging services andinformation over computer networks. WOM marketing is the informal means of disseminatinginformation from one client to another without the intervention of a marketer. As a result, the study'sgoal is to concentrate on one sort of WOM online user-generated evaluations, despite the fact that othertypes of WOM are equally worthy of inquiry. This study examined the variables that impact and hinder ecommercedeployment among SMEs in the NCR. The convenience sampling strategy was used tochoose 10 SMEs for the sample size. The Statistical Package for Social Sciences (SPSS) software wasused to provide analyses on demographic data, e-commerce benefits, barriers, perceived characteristics,and adoption based on study objectives. Profiles of SMEs will be displayed, along with frequency andpercentage distribution. T-test and ANOVA analyses were also used to evaluate the benefits anddrawbacks of e-commerce for various demographic groups. Despite the fact that other types of WOM areas deserving of inquiry, the current study focuses on one sort of WOM online consumer-generatedevaluations.

Priority based inter-twin communication in vehicular digital twin networks

ABSTRACT With the advancement and boom of autonomous vehicles, vehicular digital twins (VDTs) have become an emerging research area. VDT can solve the issues related to autonomous vehicles and provide improved and enhanced services to users. Recent studies have demonstrated the potential of using priorities in acquiring improved response time. However, since VDT is comprised of intra-twin and inter-twin communication, it leads to a reduced response time as traffic congestion grows, which causes issues in the form of accidents. It would be encouraging if priorities could be used in inter-twin communication of VDT for data sharing and processing tasks. Moreover, it would also be effective for managing the communication overhead on the digital twin layer of the cloud. This paper proposes a novel priority-based inter-twin communication in VDT to address this issue. We formulate the problem for priorities of digital twins and applications according to their categories. In addition, we describe the priority-based inter-twin communication in VDT in detail, and algorithms for priority communication for intra-twin and inter-twin are designed, respectively. Finally, experiments on different priority tasks are conducted and compared with two existing algorithms, demonstrating our proposed algorithm's effectiveness and efficiency.

A modified CNN-IDS model for enhancing the efficacy of intrusion detection system

As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.

Securing Data Transmission from Adversaries in Cybersecurity WSN Using Efficient Key Management Techniques

Concern over cyber security is becoming more and more prevalent in today's international politics. National security is now evaluated not only in terms of military might or economic might, but also in terms of technological innovation. The fact that today's society is heavily dependent on computer communication and control systems, which opens up infinite opportunities, shows how important technology is in the national security area. By effectively changing the keys, the suggested work aimed to provide WSN with complicated security solutions that would ensure data integrity, security, and genuine transmission for many additional network operations through structured cryptography approaches. The study's protective measures can be applied to a variety of WSN configurations, including heterogeneous WSNs with and without mobility aid and static homogeneous WSNs. Because WSN is more limited to hardware resources, not all applications can be protected using the same method.

Exploration of Network Management Methods in Higher Vocational Computer Education

With the rapid development of information technology, higher vocational computer education is gradually turning to network management to meet the needs of the information age. This paper describes the importance of network management in higher vocational computer education. The analysis of the status quo of network management reveals the problems of lagging in management mode, the low application rate of computer networks, and the constraints of traditional teaching. In response to these problems, suggestions and discussions are put forward, including strengthening the construction of network infrastructure, improving the network management mechanism, etc., to promote education management towards a more advanced and flexible network direction.

Teenage pregnancy: when digital social networks support becoming a mother

The onset of pregnancy during adolescence brings with it upheavals for the body, identity, family and society. This particular context can make some teenagers psychopathologically fragile, while others adapt perfectly. When these mothers-to-be receive support, their adjustment to pregnancy seems to be facilitated. At present, it seems easy for teenagers to maintain contacts and thus benefit from a form of support on digital social networks. Recently discussed in the literature, the latter could represent a source of social support in this type of pregnancy, but also of support for the identity process of becoming a mother.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Feature level fusion of multi-source data for network intrusion detection

<span lang="EN-US">The generation of data, collecting, and refining in computer networks have increased exponentially in recent years. Network attacks have also grown in prevalence with this proliferation of data and are now an inherent issue in complicated networks. Current network intrusion detection systems (NIDS) have significant issues with regard to anomaly detection. Several machine learning classification approaches are used to create NIDSs, but they are not sufficiently sophisticated to reliably detect complicated or synthetic attacks, especially if working with a lot of multi-scale data. Data fusion has been used in network intrusion detection to address these issues. For network intrusion detection, we suggested a multi-source data fusion technique in this research, which combines specific features from two datasets to produce a single dataset. Also, a machine learning classifier with fewer parameters is utilized for the fused dataset. The random forest shows the best classification accuracy compared to others in this work. For the normal classification, model accuracy is 92.8%, and the proposed fusion model showed 97.3% accuracies. Furthermore, the findings show that, when compared to other cutting-edge techniques, the suggested model is substantially more effective in detecting intrusions.</span>

Secure and Flexible Data Sharing With Dual Privacy Protection in Vehicular Digital Twin Networks

Secure and Flexible Data Sharing With Dual Privacy Protection in Vehicular Digital Twin Networks

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.