• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.891-898
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• 2003

This paper analyzes and proposes a scheme that improves the performance of the RTP that is developed to support the end-to-end transmission function and QoS monitor function for real-time multimedia data transmission. Although the existing RTP module supports real-time transmission, it has some problems in guaranteeing QoS parameters. To solve this problem, we propose a new Selective Repeat Adaptive Rate Control (SRARC). The SRARC can support QoS by referring to the data transmission status from the client and then classifying the network status into three levels. It selectively transmits multimedia data and dynamically controls transmission rates based on such information as bandwidth, packet loss rate, and latency that can be calculated in data transfer phase. To verify the SRARC, we implement it in real local area networks and compare the QoS parameters of the SRARC with those of the SR and RTP By the experimental results, the SRARC shows better performance in the aspects of bandwidth usage rate, packet loss rates, and transmission delays than the existing RTP schemes.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.