Computer Communication Networks Research Articles

Research on the Application of Data Encryption Technology in Computer Network Communication Security

Research on the Application of Data Encryption Technology in Computer Network Communication Security

Identifying knowledge evolution in computer science from the perspective of academic genealogy

Academic genealogy (AG) provides valuable insights into the transmission of knowledge from mentors to mentees, revealing the evolution of knowledge within the academic community. This study explores the intricate dynamics of knowledge evolution within academic genealogies, utilizing on a dataset comprising 16,852 computer science researchers, 613,277 papers, and 11,988 mentorship relationships. By focusing on small-scale knowledge units, our analysis aims to uncover patterns of knowledge inheritance and mutation across different subfields of computer science and highlights several aspects of knowledge evolution in computer science. Firstly, computer science is characterized by strong mentorship ties, indicating the significance of knowledge transmission within the field. Additionally, there is a mix of foundational and developing areas, suggesting a field that is growing and diversifying rather than declining, as indicated by linear regression outcomes. Secondly, our research reveals a surge in collaborative knowledge exchange in computer science since 2000, with fields such as Computer-Communication Networks and Software Engineering leading in terms of output and impact. Furthermore, areas like Computer Graphics and Artificial Intelligence stand out for their depth and novelty. Thirdly, we categorize researchers into three types: roots, branches, and leaves, reflecting their role in knowledge transmission. Branch researchers tend to innovate, while leaf researchers show a combination of traditional knowledge uptake and new contributions, illustrating the dynamic flow of ideas within the field. Future research endeavors are encouraged to embrace larger datasets and further fortify our understanding of the topic.

Improvement of post BER performance in computer communication on Viterbi algorithm

Computer communication is the pivotal exchange of information and data among computing devices, exerting substantial influence over network communication quality. The conventional approach has typically centred on enhancing Bit Error Rate (BER) performance to bolster information quality during the transmission process. However, our research pioneers a novel strategy, focusing on post-BER improvement. Our innovative method prioritizes enhancing information quality at the receiver's end by implementing a unique approach. We employ the Viterbi algorithm to elevate the quality of individual words, ensuring accurate word recognition and precise comprehension. The ultimate goal is to provide end-users with an unambiguous and accurate understanding of transmitted words. Our simulation results conclusively demonstrate the effectiveness of this approach. Following the reception of words, we successfully attain the correct word information with remarkable accuracy. This approach transforms word-level information processing, safeguarding against inaccuracies and ambiguities that may otherwise compromise the integrity of data transfer. In summary, our research introduces a paradigm shift in optimizing information quality within computer communication networks. By focusing on post-BER improvements and harnessing the power of the Viterbi algorithm, we enable the receiver to decode words with precision and clarity. This breakthrough promises to enhance the overall quality and reliability of information exchange, further solidifying the foundation of modern computer communication.

Optimization of Data Encryption Technology in Computer Network Communication

Optimization of Data Encryption Technology in Computer Network Communication

DDoS2Vec: Flow-Level Characterisation of Volumetric DDoS Attacks at Scale

Volumetric Distributed Denial of Service (DDoS) attacks have been a severe threat to the Internet for more than two decades. Some success in mitigation has been achieved based on numerous defensive techniques created by the research community, implemented by the industry, and deployed by network operators. However, evolution is not a privilege of mitigations, and DDoS attackers have found better strategies and continue to cause harm. A key challenge in winning this race is understanding the various characteristics of DDoS attacks in network traffic at scale and in a realistic manner. In this paper, we propose DDoS2Vec, a novel approach to characterise DDoS attacks in real-world Internet traffic using Natural Language Processing (NLP) techniques. DDoS2Vec is a domain-specific application of Latent Semantic Analysis that learns vector representations of potential DDoS attacks. We look into the link between natural language and computer network communication in a way that has not been previously studied. Our approach is evaluated on a large-scale dataset of flow samples collected from an Internet eXchange Point (IXP) in one year. We evaluate the performance of DDoS2Vec via multi-label classification in a Machine Learning (ML) scenario. DDoS2Vec characterises DDoS attacks more clearly than other baselines - including NLP-based approaches inspired by recent networks research and a basic non-NLP solution.

Research on the Current Situation and Development Trend of Computer Network Communication in the New Era

Research on the Current Situation and Development Trend of Computer Network Communication in the New Era

Retracted: Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Retracted: Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

A novel Congestion Control algorithm based on inverse reinforcement learning with parallel training

With the growing impact of the Internet, computer network communication has become an essential component for various industries. Congestion Control (CC) algorithms serve as the backbone of network communication, significantly affecting network quality. However, designing a CC algorithm that performs optimally across diverse network environments presents substantial challenges. The task of redesigning and optimizing CC algorithms for specific network environments demands both expert experience and a substantial workforce. In this paper, we proposed an inverse reinforcement learning (IRL) algorithm that can use expert data to guide the self-optimization of the CC model in specific network environments. To enhance model training efficiency, we propose a parallel training framework and a visualization analysis tool, enabling distributed training and real-time control level analysis. In the experiments, we assess the performance of 16 algorithms across 3 network scenarios using Pantheon. Our IRL model achieves the optimal level of network performance in satellite network, enhancing throughput by 10%–23%. For delay performance, it ranks 2nd in wired network and achieves a 21%–67% improvement over traditional TCP algorithms in regular network.

Design and analysis of ultra-large capacity heterogeneous nineteen-core seven-mode fiber

The multi-core few-mode fiber based on space division multiplexing technology is one of the critical ways to address the issue that the current single-mode fiber communication capacity has reached its maximum. However, the energy crosstalk between the cores is an important reason for the deterioration of signal quality. Consequently, this paper designs a nineteen-core seven-mode fiber with more transmission channels, and the auxiliary structures such as trench, high and low refractive index double rings, and air holes can effectively reduce intra-core crosstalk and inter-core crosstalk. After optimization, each core of the nineteen-core fiber can transmit seven LP modes, and the effective refractive index difference between adjacent modes is greater than or equal to 10−3. The optical fiber provides 228 transmission channels for the entire transmission system, the inter-core crosstalk of the highest order LP41 mode is lower than −30 dB/100 km at 1550 nm. Based on the above, the proposed optical fiber has excellent optical properties and can be applied to data centers, optical sensors, high-speed computer communication networks, and other applications.

DIJKSTRA ALGORITHM IN DETERMINING THE SHORTEST ROUTE FOR DELIVERY SERVICE BY J&T EXPRESS IN BANDUNG

The determination of the shortest route can be done using various methods, one of which is the Dijkstra algorithm. This algorithm is often used in routing problems with minimum weight in computer networks, communication networks and transportation networks. There are several applications of the Djikstra algorithm, namely solving the problem of the shortest path of neutrosophy, mapping tourism potential, and developing geographic information systems in the health sector. In this research, Dijkstra algorithm will be applied to optimize the mileage of the delivery of goods packages. Data and information namely the address, weight and number of customer packages carried by a courier in one delivery trip are obtained from the drop point of PT. J&T Express in Sarijadi area of Bandung City. Meanwhile, data about mileage is obtained from the Google Maps application. All this data is used to construct an initial model graph that is a connected weighted graph, where the location of a drop point or a customer is a vertex and a road connecting two locations is an edge on the graph. The weight in this graph is the mileage from the drop point to the customers or from one customer to another. Then, the Djikstra algorithm is run on this graph where the drop point is the starting point of the route, so that the courier visits all customers and returns again to the drop point. The resulting route is a cycle in the graph which is the shortest closed route at 1890 meters.

Retracted: Application of Information Encryption Technology in Computer Network Communication Security

Retracted: Application of Information Encryption Technology in Computer Network Communication Security

Tools and Techniques for Teaching and Research in Network Design and Simulation

Computer networking is taught for some time at universities. A network is a complex mix of applications, communications protocols and link technologies, traffic flows and routing algorithms. Teaching the networking concept, the network design process is a challenging task, requiring designers to balance user performance expectations with costs and capacities. One of the obvious approaches employed to deal with its complexity is through the use of modelling and simulation techniques. This paper examines three best educational tools that can be used in the Computer Networking course at Cardiff Metropolitan University or any University for educational purpose. The paper demonstrates that a successful teaching programme needs to combine a range of teaching tools to achieve its aims. Analysing various aspects of the course provides evaluation of the overall success of the tools. Many network design and simulation tools have been studied. Based on our research and experiment, the best educational tools were selected that could be used for the purpose of teaching in the area of computer communications networks. The wide area network modelling tool Delite, the network simulator ns-3, the topology generation tool Brite can be used together to teach students the networking concepts. These tools can not only help students to understand computer network principles but also improve their network practical skills. Currently there are various tools available that are created by different companies and groups of researchers in academia and that are intended for use as practical and/or educational tools for network design including security issues. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. Very often, such tools are addressing different issues in the network design process which are complementary to each other. It would be useful for learning and improving the network design process by allowing the use of a variety of tools.

Research on Online Framework of Distributed Computer Communication Network Course Training

The article mainly analyzes the structure of distributed computer network and the specific measures for its optimization. The distributed computer network includes the connections and servers of different clients, and the architectural style is introduced into the existing "computer communication network excellent course system", which makes the system more concise and standardized, and improves the scalability of the system. At the same time, any system can communicate with other systems. Compared with other network structures, the multi-version parallel development mode is adopted to ensure the development of new functions and the improvement of stability. This enables new requirements functions to be quickly responded to in high versions, while reducing their impact on the stability of the framework itself.

Divergence Based Feature Selection for Pattern Recognizing of the Performance of Intrusion Detection in Mobile Communications Merged with the Computer Communication Networks

The Feature Selection concept is the procedure where in the data is simplified removing the irrelevant features. Divergence method is another strategy of method where in the relation among the attributes and class is measured to understand their contribution to performance. Nowadays the mobile network is integrated with the other networks like the computer networks transmitting all kinds of data leading to attacks in networks known as intrusion in computer networks equally applicable to mobile communications. So in this paper the intrusion detection method involving the other other mode of communication is considered in mobile interaction network. The proposed algorithm performs feature selection using the divergence evaluation method to reduce the feature set. A 10% KDDCUP99 data set was used for the evaluation of the proposed algorithm, and performance metrics were evaluated using the C4.5 classifier. The metrics TPR, FPR and consistency were compared with the mutual information based DMIFSA, RMIFSA, MMIFSA methods and the proposed method is implemented on Python 3.8 that proved to achieve the high accuracy of 99.94% as compared to other methods and also reduce the redundant features. The consistency in Accuracy is maintained almost from 4 features to 10 features in proposed method as compared to other methods that indicates the stability of the system is achieved.

Computer Network Communication Security Encryption System Based on Ant Colony Optimization Algorithm

With the increasing number of computer network users, the problem of network communication security has become more and more serious. Encryption technology, as one of the key technologies for securing communication, has important research and application values. However, the security of conventional encryption methods is gradually challenged, and at the same time, traditional encryption algorithms require a large amount of computing resources, which makes it difficult to realize real-time encryption and decryption operations. Therefore, how to improve the confidentiality and efficiency of computer network communication security encryption system has become a current urgent problem to be solved. This paper firstly discussed the analysis of its application in the computer network communication security encryption system based on the ant colony optimization (ACO) algorithm. Through the theoretical analysis of the ACO algorithm in terms of bioheuristic optimization algorithm and the analysis of the characteristics of the algorithm, the optimization scheme of key distribution based on the ACO algorithm was discussed. Then, the differences between the ACO algorithm and the traditional encryption algorithm in terms of encryption duration, key distribution time, encryption strength and random number generation speed were further analyzed and verified by experimental data. The data results showed that the key distribution of the encryption system using the ACO algorithm had higher security and better performance compared with the traditional key distribution algorithm. By counting and comparing the data, it was found that the ACO algorithm showed very obvious advantages in terms of encryption and decryption time, key distribution time, encryption strength and random number generation. This indicates that the ACO algorithm has very important application prospects in computer network communication security encryption systems.

Remote Data Security Monitoring Technology for Computer Networks Based on Machine Learning Algorithms

In the field of computer network communication, there are various types of data, and these data have strong commercial value. Therefore, it is particularly important to apply them to relevant security monitoring. This article analyzes and studies the problems in remote detection of computer networks using machine learning algorithms. Firstly, three key technologies based on classifiers, feature extraction, and association rule recognition were introduced. Secondly, how to use machine learning methods to evaluate the performance indicators and operational effectiveness data statistics of computer network security monitoring systems was explained. Finally, this article tests the monitoring technology of the system, and the test results show that the security of the technology for data monitoring can reach over 94%.

A New Toolbox for Scheduling Theory

Queueing delays are ubiquitous in many domains, including computer systems, service systems, communication networks, supply chains, and transportation. Queueing and scheduling theory provide a rigorous basis for understanding how to reduce delays with scheduling, including evaluating policy performance and guiding policy design. Unfortunately, stateof- the-art theory fails to address many practical concerns. For example, scheduling theory seldom treats nontrivial preemption limitations, and there is very little theory for scheduling in multiserver queues. My thesis presents two new, broadly applicable tools that greatly expand the reach of scheduling theory, using each to solve multiple open problems. The first tool, called "SOAP", is a new unifying theory of scheduling in single-server queues, specifically the M/G/1 model. SOAP characterizes the delay distribution of a broad space of policies, most of which have never been analyzed before. Such policies include the Gittins index policy, which minimizes mean delay in low-information settings, and many policies with preemption limitations. The second tool, called "WINE", is a new queueing identity that complements Little's law. WINE enables a new method of analyzing complex queueing systems by relating them to simpler systems. This results in the first delay bounds for Shortest Remaining Processing Time (SRPT) and the Gittins policy in multiserver queues, specifically the M/G/k model. This abstract gives a brief overview of my thesis, describing what the SOAP and WINE tools do, the key ideas underlying them, and the open problems they help solve.

Game Changer in Cybersecurity: Quantum Cryptography

With digital technology and increasingly linked economies comes a new set of risks from a slew of cyber-attacks. To improve cyber resilience, a cybersecurity capability system comprised of People, Process, and Technology is required to control the ongoing advancement of science and technology, particularly the quantum computer. As a result, attempts have been undertaken to lay new groundwork for cryptography research in computer communication networks. One of these attempts resulted in the creation of quantum cryptography technology, the security of which is based on quantum mechanics rules. Cyberspace security has emerged as the most important problem for the Internet in the foreseeable future. The objective of this research paper is to discuss the flaws and security problems in contemporary encryption, quantum cryptography essential concepts, real-world application use of this technology, and lastly the future direction of quantum cryptography.

Research on the application of search algorithm in computer communication network

Abstract This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.

Computer Hardware and Communication Network Technology in Internet of Things

Computer Hardware and Communication Network Technology in Internet of Things