Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN....
The rapid development of computer network technology highlights the importance of understanding computer networks, especially for students in higher education. Informatics engineering and computer science programs at universities focus on teaching networking concepts, with Cisco Packet Tracer emerging as an effective tool for network simulation. This research aims to explore the use of Cisco Packet Tracer in the computer network learning process, emphasizing its impact on students’ understanding of network design and configuration. The study employs a quantitative research approach, using simple random sampling to select participants from computer network courses. Research instruments include pre- and post-tests to measure knowledge gain and a survey questionnaire to assess students' learning experiences. Data analysis, using descriptive statistics, reveals that students showed a significant improvement in their understanding of network concepts after using Cisco Packet Tracer. The findings indicate that simulations enhanced student engagement, simplified the learning process, and provided a cost-effective alternative to hardware-based network labs. In conclusion, Cisco Packet Tracer is an effective tool for improving the learning experience in computer network education, offering both educational and economic benefits....
Since the beginning of the 21st century, the development of computer networks has been advancing rapidly, and the world has gradually entered a new era of digital connectivity. While enjoying the convenience brought by digitization, people are also facing increasingly serious threats from network security (NS) issues. Due to the significant shortcomings in accuracy and efficiency of traditional Long Short-Term Memory (LSTM) neural networks (NN), different scholars have conducted research on computer NS situation prediction methods to address the aforementioned issues of traditional LSTM based NS situation prediction algorithms. Although these algorithms can improve the accuracy of NS situation prediction to a certain extent, there are still some limitations, such as low computational efficiency, low accuracy, and high model complexity. To address these issues, new methods and techniques have been proposed, such as using NN and machine learning techniques to improve the accuracy and efficiency of prediction models. This article referred to the Bidirectional Gated Recurrent Unit (BiGRU) improved by Gated Recurrent Unit (GRU), and introduced a multi model NS situation prediction algorithm with attention mechanism. In addition, the improved Particle Swarm Optimization (PSO) algorithm can be utilized to optimize hyperparameters and improve the training efficiency of the GRU NN. The experimental results on the UNSW-NB15 dataset show that the algorithm had an average absolute error of 0.0843 in terms of NS prediction accuracy. The RMSE was 0.0932, which was lower than traditional prediction algorithms LSTM and GRU, and significantly improved prediction accuracy....
In this modern era, platforms for digital/social media and video games are growing daily. People are becoming dependent on them from all ages and with many positive aspects, but there are drawbacks as well, one of which is cyberbullying. Cyberbullying is a form of bullying that uses technological platforms to bully others. It has effects on victims mentally, emotionally, and physically, which include low self-esteem, acting violently, despair, increased stress/anxiety, depression, self-harming/suicide, etc. Findings from this research study justify that it affects young people more, impacting their emotional development and overall safety. Real-time cyberbullying detection identifies and protects the target from further abuse and its effects. This study aids in determining the seriousness of the issue and the vulnerabilities that individuals can take advantage of to bully others. Additionally, it will help to understand how various features of cyberbullying detection function assist in developing a strong and trustworthy system and making a healthy online community. Natural Language Processing (NLP) models assess the textual content and analyze hashtags and comments. Similarly, image context is analyzed using Optical Character Recognition (OCR), which converts images into a machine-readable format for further examination. There are also Deep Neural Network models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM). CNN is utilized for text/picture classification, LSTM is used for long-term dependency learning, and BLSTM expands the network’s input by encoding data in both forward and backward directions. Classifiers like Support Vector Machine (SVM) and Naïve Bayes help detect cyberbullying. A working cyberbullying detection system can detect cyberbullying on multiple platforms. A deeper understanding of each machine learning algorithm allows one to build a model that improves upon their predecessors. With models being developed for different attributes providing results with high accuracy, the cyberbullying detection system contributes by leading us to a healthier online community....
Sometimes, in practice, simple solutions of quick preliminary estimation of basic characteristics of a computer network are needed. In this aim, the backbone subnet and server set of wide area computer networks are examined. Based on Jackson’s partitioning theorem and considering the linear dependence of the costs of channels, routers, and servers on their performance, a simplified analytical model for these components of the network is defined. Using this model, two optimization problems are formulated: minimizing the average response time to user requests of data processing and minimizing the summary cost of servers, channels and routers of the computer network. For both problems, analytical solutions regarding the necessary performances of channels, routers and servers are obtained. As expected, in the obtained analytical solutions, the equations for the optimization criteria of the two problems coincide, only their form being different. Calculations of performances according to these solutions are simple and can be done, for example, in MS Excel. Because the obtained in this way performances are positive real numbers, and the allowed performances of concerned entities are discrete ones, further adjustment of the solution in question, depending of the case, may be necessary. For such an adjustment, two algorithms are proposed. One of them solves the problem by reducing it to that of backpack. Another solves the problem based on the use of resource concentration rule....
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