Computer Networks Research Articles

Congestion control in internet of things (IoT) using auction theory

The Internet of Things (IoT) facilitates data transmission through communication networks, preventing congestion when input data rate exceeds output, and congestion control in computer networks modulates traffic entry. This paper proposes a fusion of auction theory with reinforcement learning as a means of managing congestion in the IoT. The proposed technique seeks to enhance network performance by utilizing object trustworthiness evaluation and auction-based route selection to manage congestion during data routing. The suggested method calculates the believability of objects by analyzing their historical performance in data forwarding and congestion avoidance, utilizing a learning automaton. The auction approach is employed to determine the most efficient ways for transmitting data. The IoT topology is initially organized into a collection of dependable links known as the Connected Dominating Set (CDS). Active objects employ the learning automata model to assess the reliability of their neighbors. The auction model ultimately chooses the optimal route based on characteristics such as credibility, energy, and delay. The experimental results demonstrate that the proposed methodology surpasses existing comparison methods in the initial scenario, exhibiting a 24.13% reduction in energy usage.

A Study on OTT Support Project and Policy Improvement Plan in Korea to Revitalize K-Drama; Focusing on France and Canada

With the recent development of Internet technology and the rapid evolution of digital networks, various types of video content distribution platforms have emerged.[1] In particular, in the era of COVID-19, video contents such as Korean movies and dramas are growing with worldwide attention through the global OTT platform.[1] However, after COVID-19, the Korean media and content industries are experiencing more difficulties than ever in 2024, as they have not been able to cope with the global environment compared to the Korean media and content industries. Therefore, in preparation for the media and content industry environment, which has many variables and is constantly changing under the global environment, we would like to once again check the drama-related support policy currently implemented by the government and propose a plan to revitalize the K-drama industry.

Blockchain Technology: Benefits, Challenges, Applications and Future Direction

Blockchain technology has rapidly evolved from its origins in cryptocurrency to become a fundamental element across diverse sectors demonstrating its potential to transform traditional processes. Blockchain technology is a decentralized digital ledger system that securely records and verifies transactions across a network of computers, fostering transparency and trust without the need for intermediaries. This review paper examines the multifaceted applications of blockchain technology highlighting its capabilities in enhancing transparency, security and efficiency. Key applications include financial services where blockchain facilitates faster and more secure transactions through smart contracts and decentralized finance platforms. The healthcare sector benefits from blockchain's ability to securely manage patient data ensuring compliance with regulations while improving accessibility for authorized providers. Blockchain technology enhances business operations by enabling smart contracts that automate transactions and reduce the need for intermediaries, leading to cost savings and increased efficiency. In government, blockchain can improve public trust and transparency by providing secure, tamper-proof voting systems that ensure the integrity of electoral processes. Individuals can maintain greater control over their personal information allowing them to share data selectively while ensuring privacy and reducing the risk of data breaches. This paper emphasizes the significant impact of blockchain technology across various sectors. It calls for cooperation among different stakeholders to make the most of its advantages while addressing its risks. As blockchain continues to develop, it has the potential to change economic systems and improve efficiency worldwide.

Indonesian news article authorship attribution multilabel multiclass classification using IndoBERT

<p class="p1">Recent developments in technology have made it easier to produce digital con- tent, especially textual articles. But, it has a negative impact in the form of a rising public skepticism of digital data due to plagiarism. Indonesia, one of the world’s most populous countries, is not resistant to this problem. To resolve it, the authorship attribution (AA) task must be executed. However, there has been little investigation on AA for Indonesian articles. As a result, this research applies the AA task to an Indonesian digital news articles dataset. Continuing the previous research, dataset modification was carried out to increase data com- plexity by adding a new class, namely the author’s gender, and also by balancing the distribution of data versus labels to minimize potential overfitting, and model hyper-parameter configurations were carried out to enhance the results gained. This research successfully applied the IndoBERT model to the Indonesian AA task, yielding results in the form of precision = 0.92, recall = 0.90, and F1-score = 0.91. These results indicate that the Indonesian AA task has a lot of potential for development since it identifies writing patterns that may benefit the forensic field, detect plagiarism, and analyze Indonesian texts.</p>

Using of Deep Learning in Beamforming Antenna Array

Digital beamforming (DBF) is a crucial technology for large antenna arrays, offering precise control over beam steering. This research introduces a novel method to enhance millimeter wave transmission by incorporating DBF with long-term memory (LSTM) based deep learning Our system utilizes digital signal processing and LSTM networks to optimize beamforming parameters instead of relying on traditional analog beamforming. The objective is to achieve high spectral efficiency. The methodology is executed in the programming language MATLAB and the obtained simulation outcomes validate a substantial enhancement in the metrics that evaluate performance, thereby demonstrating the potential of combining digital beamforming (DBF) with Long Short-Term Memory (LSTM) for forthcoming communication systems. Furthermore, the inclusion of LSTM in the process of digital beamforming presents a comprehensive comprehension of the proposed approach, which imparts valuable insights for advanced communication technologies. To substantiate the efficacy of the technique, several illustrative examples are employed to steer the beam pattern in the desired direction.

Revolutionizing Fault Detection in Self-Healing Network via Multi-Serial Cascaded and Adaptive Network

Self-Healing Network (SHN) plays a crucial role in the realm of digital networking and the telecommunications industry. Fault detection in SHN is a critical aspect of network management and maintenance, which acts as a pivotal role in maintaining network health and minimizing disruptions. The SHN networks aim to manage faults and system failures by automating the detection process and pinpointing their origins. This proactive approach is essential to maintain network integrity and ensure a seamless user experience. In this paper, a novel multi-Serial cascaded and Adaptive Network based fault Detection in SHN (SAND-SHN) technique has been proposed for detecting faults in the SHN network. The proposed method uses the Eigen-Entropy Synthetic Minority Oversampling Technique (EE-SMOTE) to balance imbalanced data for classification and the Multi-Serial Cascaded and Adaptive Network (MSCAN), which integrates deep learning (DL) techniques to attain the final classified outcome. The proposed SAND-SHN method has been evaluated using a Python environment in terms of specific parameters such as accuracy, precision, recall, specificity, and F1-Score. The proposed technique has been evaluated using two datasets as EFCD dataset, and the SFDD dataset. The proposed SAND-SHN technique achieves a higher accuracy of 74% for RSO-MSCAN, 39.2% for DMO-MSCAN, 48.8% for BFGO-MSCAN, and 43.6% for FHO-MSCAN respectively.

Designing Windows-Based Learning Applications Using Computer Network Material

This article discusses the design of a Windows-based learning application for computer network hardware. In the context of education, providing computer network hardware is often a challenge, especially in terms of understanding complex concepts. Therefore, interactive and interesting learning materials are needed to help students understand the material better. The application is designed using an interactive multimedia approach, combining text, images, animations, and videos to present information in a computer network. The design process includes needs analysis, user interface design, and development of program-related content. Application testing is carried out to ensure that the application meets learning objectives and can be used effectively by students. The results of the study show that this application not only improves students' understanding of computer networks but also provides a more enjoyable learning experience. Therefore, hopefully this learning application can be an effective tool in the teaching and learning process in educational environments.

Digital Preservation and Network Security Protection: A Case Study of Landscape Painting in Ming Dynasty

In the global wave of digitalization, the digital preservation of Chinese Ming Dynasty landscape paintings has achieved remarkable results. According to statistics, major museums in China have scanned more than 60% of Ming Dynasty landscape paintings with high precision, and the digital preservation work covers a wide range. However, the accompanying cybersecurity challenges cannot be ignored either. In recent years, cyber attacks on digital platforms of cultural heritage have occurred frequently. Worldwide, the number of such attacks has exceeded 3 million every year, of which attacks on works of art account for as high as 15%. Faced with this grim situation, China has taken a series of measures to strengthen network security protection. By upgrading encryption technology, optimizing network security protection systems, and conducting regular network security training and drills, China has made remarkable progress in ensuring the security of digital works of art. According to the latest data, since the implementation of enhanced protection measures, the number of cyber attacks on the digital platform of landscape painting in Ming Dynasty in China has decreased by 35%, effectively protecting precious cultural heritage from cyber threats.

Pelatihan Sistem Cloud Computing Menggunakan Layanan Dropbox Bagi Mahasiswa Baru

Cloud computing is a combination of computer technology and internet-based development. Cloud computing uses the term "cloud", or cloud, to describe the internet, which is often depicted on computer network diagrams. Cloud computing capacity is an information technology service that can be accessed via the internet without knowing the infrastructure behind it. This opens up the possibility of accessing and using technology more efficiently and flexibly. The development of information technology, especially cloud computing, has had a significant impact in various fields, including education. Cloud computing changes the way information technology is distributed and accessed, providing opportunities for educational institutions to access data and educational resources more efficiently and flexibly. It also helps educational institutions to focus more on core educational processes without having to manage technology infrastructure directly. Teaching about cloud computing is becoming increasingly important. Because through an introduction to the basic concepts, types, benefits and practical applications of cloud computing, students can understand the importance of this technology in the modern world. Thus, teaching and training measures such as those described in this study are expected to help increase the understanding and application of cloud computing among students, preparing them for the challenges and opportunities in the ever-evolving digital era.

Computer Network and Database Security Technology Optimization

With the continuous development of computer network technology, its applications in daily life and work have become increasingly widespread, greatly improving efficiency. However, certain security risks remain. To ensure the security of computer networks and databases, it is essential to enhance the security of both through optimization of technology. This includes improving management practices, optimizing data processing methods, and establishing comprehensive laws and regulations. This paper analyzes the current security risks in computer networks and databases and proposes corresponding solutions, offering reference points for relevant personnel.

The Impact of Technology and Social Media on Marriage and Divorce: A Study in Besilam Village, Langkat Regency

In today's rapidly evolving digital era, technology and social media have become integral parts of daily life. From connecting people worldwide to shaping how we communicate, work, and play, these advancements have profoundly transformed social, cultural, and economic dynamics. This article explores the significant role of technology and social media in the modern world and examines their influence on how we live, interact, and share information. While social media serves as an effective two-way communication tool, its impact can vary depending on how it is used. The concept of communication and information-sharing via computer networks, which predates modern social media, marked the foundation for what we now recognize as social media platforms. As technology and social media continue to advance, they have given rise to new societal phenomena, including divorces stemming from relationships formed through social media interactions. Remarkably, marriage ceremonies can now be conducted via video calls, and divorces can be finalized through video conferences. The ongoing development of technology has significantly broadened societal knowledge. The emergence of diverse social media platforms has further enabled people to expand their social networks and forge new friendships.

The Planning and Design of Digital Smart Campus

Under the background of “Digital Liaoning, Smart Province,” as the application of a new generation of network information technology in colleges and universities, the digital smart campus is an important part of digital Liaoning education informationization. Combined with the overall actual situation of the university campus, in line with the principle of “People-oriented, Service-oriented, Information Interoperability and Data Sharing,” through the idea of “management + service,” the traditional application system with management as the core to the service as the core is realized. On the basis of digital information and networks, a new digital smart campus is built based on several information technologies. From the aspects of architecture design, platform and application integration, systematic overall design and scientific and reasonable deployment, to create an efficient one-stop digital intelligent information service platform to fully optimize the use of digital information resources, to provide timely, accurate and efficient campus information services for teachers, students and staff, and to provide cross-departmental business management and convenient information services for the whole school users through the virtual digital means for display and publicity. The overall planning of the campus is carried out step by step, and the campus network, digitalization, virtualization and intelligence are gradually realized. Expand the time and space dimension of the real campus, improve the overall operation efficiency, and then improve the quality of talent training, management level and management efficiency.

Application and Challenge of Edge Computing Based on 5G Communication in Information and Communication Systems

With the rapid development of information technology, 5G communication technology has gradually entered real life, among which the application of edge computing is particularly significant in the information and communication system field. This paper focuses on using edge computing based on 5G communication in information and communication systems. First, the study analyzes the importance of combining edge computing technology with 5G communication technology, and its advantages, such as high efficiency and low latency in processing large amounts of data. The study then explores multiple application scenarios of edge computing in information and communication systems, such as integrated use in the Internet of Things, intelligent transportation, telemedicine and Industry 4.0. The research method is mainly based on theoretical analysis and experimental verification, combined with the characteristics of the 5G network to optimize the edge computing model and test the performance of edge computing in different scenarios through experimental simulation. The results show that edge computing significantly improves the data processing capacity and response speed of ICS in a 5G environment. However, there are also a series of challenges in practical application, including data security and privacy protection, the complexity of resource management and allocation, and the guarantee of quality of service (QoS). Through the case analysis and problem analysis, the paper puts forward the corresponding solution strategies, such as strengthening the data security protocol, introducing the intelligent resource scheduling system and establishing a multi-dimensional service quality monitoring mechanism. Finally, this study points out that the deep integration of edge computing and 5G communication will continue to promote the innovative development of information and communication systems, which has a far-reaching impact and important practical significance for promoting the transformation and upgrading in the field of information technology.

APPROACH TO DETECTION OF ANOMALOUS NETWORK TRAFFIC USING LOF AND HBOS ALGORITHMS

The article is devoted to the problem of detecting anomalies in modern computer networks, which is one of the main threats to cyber security. With the development of Internet technologies, the number of devices and the volume of network traffic are constantly increasing, which leads to an increase in the risk of various cyber threats, such as DDoS attacks, zero-day attacks, and exploitation of protocol vulnerabilities. Abnormal network traffic can result from malicious activity and technical malfunctions, such as configuration errors or hardware failures. Specialised algorithms and methods of analysing large volumes of data are used to detect such threats. The paper considers the main methods of detecting anomalies in network traffic, including classical approaches and modern deep and machine learning methods. Special attention is paid to the efficiency of using methods based on convolutional neural networks, long-term memory and their combinations to detect anomalies. An analysis of the disadvantages and advantages of various approaches to detecting anomalous traffic, such as high computational requirements and the complexity of setting up models, is performed. Still, their effectiveness in analysing large volumes of data is noted. One of the main methods used for anomaly analysis is the local outlier algorithm, which compares the density of objects with their neighbours, allowing for the detection of anomalies in regional segments of the data. Another method is histogram-based outlier estimation, which is faster and more efficient using one-dimensional histograms for each variable. The work also explores the application of unsupervised machine learning methods, which allows for analysing network traffic in real time without the need for prior labelling of data. The article also considers the prospects of further testing the proposed methods in real networks. The combined use of LOF and HBOS balances anomaly detection accuracy and data processing speed, essential to ensure continuous system operation in high-load networks. The implementation of similar solutions in actual conditions requires further research, particularly regarding optimising the use of computing resources and adapting methods to the specific conditions of the network environment. Thus, the paper presents a thorough analysis of modern approaches to detecting anomalies in network traffic and substantiates the feasibility of their application in actual conditions to increase the effectiveness of cyber security.

A RESPONSABILIDADE PENAL NOS CRIMES CIBERNÉTICOS

The objective of this article is to address cybercrimes and their responsibility in the criminal sphere, aiming to substantiate the importance of classifying crimes committed in virtual environments, a fact of great relevance in the relationships and interactions of today’s society. In this sense, cybercrimes are illegal practices carried out in the digital environment, using computers, devices and networks connected to the internet. In this way, criminal responsibility in cybercrimes is to establish legal standards and procedures to hold individuals who commit crimes in the digital environment accountable. Clearly define what actions constitute cybercrimes and what the legal consequences are for the perpetrators of these crimes. This may include defining different types of cybercrime, such as online fraud, cyberterrorism, among others, and the penalties associated with each.

REGULAÇÃO DE CONVIVÊNCIA VIRTUAL: DESAFIOS PARA A LIBERDADE DE EXPRESSÃO NO BRASIL

The term "web" in English refers to the global computer network, which has significantly impacted human life and social relations. The internet has facilitated various human activities, from communication to education, and has also led to an increase in criminal activities and rights violations. This study aims to contribute to reflection on the regulation of content on social networks, reviewing the literature on how social media platforms can combat fake news and hate speech, guaranteeing freedom of expression, with the aim of analyzing the dynamics between regulation of virtual coexistence on social networks and autonomy of expression in Brazil. The research was carried out through a literature review and will investigate the impact of current laws and practices on the balance between criminal control, hate speech and civil liberties, as well as identifying Brazilian and international regulations.

Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform, this paper designs and implements a highly efficient and accurate scoring system for subjective questions, focusing primarily on questions in the field of computer network technology. The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques, such as supervised fine-tuning. In the data preparation phase, a comprehensive collection of subjective data related to computer network technology is gathered, cleaned, and labeled. During model training and evaluation, optimal hyperparameters and tuning strategies are applied, resulting in a model capable of scoring with high accuracy. Evaluation results demonstrate that the proposed model performs well across multiple dimensions—content, expression, and development scores—yielding results comparable to those of manual scoring.

REHAS: Robust and Efficient Hyperelliptic Curve‐Based Authentication Scheme for Internet of Drones

ABSTRACTInternet of Drones (IoD) is one of the most beneficial and has many versatile applications like Surveillance and Security, Delivery and Logistics, Environmental Monitoring, Agriculture, and so forth. The IoD network is crucial for collecting sensitive data like geo‐coordinates, vehicle traffic data, and property details while surveying the various deployment locations in smart cities. The communication between users and drones can be compromised over insecure wireless channels by multiple attacks such as Man‐in‐the‐middle‐attack, Denial of Service, and so forth. Many schemes have already been propounded in the field of IoD. Still, many of them cannot address the resource constraints problem of drones, and existing protocols have higher computation and communication costs. Therefore, this paper has proposed a robust and efficient Hyper‐Elliptic Curve‐based authentication scheme (REHAS), which provides a session key for secure communication. Artificial Identities are generated using a hash function and random numbers. Fuzzy Extractor is used for user biometric authentication, which makes the smart device secure when lost. HECC is used with a smaller bit size of 80 bits rather than ECC of 160 bits. The security of the REHAS has been ensured using Scyther simulation. Furthermore, the resilience, safety, and robustness of REHAS are ensured by Informal security analysis. Lastly, a comparative study of the REHAS has been performed with other related Authentication and key agreement (AKA) protocols regarding communication cost, Computation cost, and security features, demonstrating that REHAS incurred less computation cost (6.7171 ms), communication overhead (1696 bits), and energy consumption (22.5 mJ) than other existing AKA schemes.

Holography of transmission lines: Insights of continuous MERA and AdS/CFT

This study examines the holographic representation of the quantum theory of transmission lines, which play a crucial role in quantum computing and quantum information. By utilizing Yurke and Denker’s quantum circuit network theory within the framework of continuous Multi-scale Entanglement Renormalization Ansatz (cMERA) in AdS space, we analyze the quantization and interactions of transmission lines. The metric is revealed to be described by the inductance of the quantum circuit, which is AdS space in its 0-limit. These results provide new insights into handling and controlling complex phenomena in quantum circuits, potentially advancing the understanding of quantum computing and quantum communication.

Unveiling shared therapeutic targets and pathological pathways between coronary artery disease and major depressive disorder through bioinformatics analysis.

Coronary artery disease (CAD) is a predominant cardiovascular condition influenced by risk factors, with an emphasis on major depressive disorder (MDD). However, the shared mechanisms and therapeutic targets for CAD and MDD remain incompletely comprehended. Functional enrichment analyses were conducted to investigate the pathways associated with the differentially expressed genes (DEGs) in the CAD and MDD datasets. Hub genes were identified utilizing the Protein-Protein Interaction network and Cytoscape software. The single sample gene set variation analysis was applied to assess immune cell infiltration in the CAD and MDD datasets. Weighted gene co-expression network analysis and molecular biological experiments were executed to evaluate these hub genes. Molecular docking was conducted to identify drug candidates targeting these hub genes. The overlapping DEGs between the CAD and MDD datasets were mainly enriched in the Herpes simplex virus 1 infection and the NF-kappa B signaling pathways. CDC42, NDUFB3, and TXN were validated within the eigengenes of the blue module, which exhibited a significant association with the CAD phenotype. The drug candidate GS-9620 was identified as a potential protective agent against both disorders. In conclusion, CDC42, NDUFB3, and TXN held potential as molecular biomarkers and therapeutic targets for the simultaneous treatment of CAD and MDD.