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A novel graph convolutional networks model for an intelligent network traffic analysis and classification

International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Olabanjo, Olusola Wusu, Ashiribo Aigbokhan, Edwin Olabanjo, Olufemi Afisi, Oseni Akinnuwesi, Boluwaji Department of Mathematics Morgan State University Baltimore MD United States Department of Computer Science Lagos State University Ojo Lagos Nigeria Department of Mathematics Lagos State University Ojo Lagos Nigeria Department of Computer Technology Yaba College of Technology Yaba Lagos Nigeria Department of Cybersecurity University of Derby Derby United Kingdom Department of Philosophy Lagos State University Ojo Lagos Nigeria Department of Computer Science Faculty of Science and Engineering University of Eswatini Kwaluseni Swaziland

network security in the midst of evolving and complex cyber-attacks is a growing concern. As the complexity of network architectures grows, so does the need for advanced methods in network traffic analysis and classification. This study explores the application of a novel Graph Convolutional networks (GCNs) to address the challenges associated with intelligent network traffic analysis. The network interactions are modeled as a graph, where nodes represent devices or IP addresses, and edges capture the communication channels between them. In this work, dataset which contains packet information of some network devices was obtained from an online repository. The data was preprocessed, normalized and label-encoded. Seven baseline models, including Feed Forward network (FFN) were developed as reference to the proposed GCN. The parameters were tuned to optimize the performance and the dataset was split into average train-test to avoid overfitting. Two convolutional fully-connected layers were used also as more could cause oversmoothing. Performance of the novel GCN was compared with the reference models. The improved GCN model gave classification accuracy of 94.3% compared to classical GCN with 92.5% and FFN with 88%. Results also showed that the enhanced GCN proposed in this study outperformed the classical GCN and FFN in precision, recall, F1 score and area under curve metrics. Through the utilization of a GCN architecture and proposed enhancements, the proposed model demonstrates notable effectiveness in accurately classifying diverse types of network traffic. This research showed the efficacy of GCNs in intelligent network traffic analysis, offering a promising approach to augmenting cybersecurity efforts in an evolving digital landscape. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cybersecurity Graph convolutional networks (gcns) Intelligent classification Machine learning network data processing network traffic analysis

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CONTROL OF BOTTLENECKS IN A network dataBASE MACHINE

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INTERNATIONAL CONF ON COMPUTING AND INFORMATION ( ICCI 90 )

作者： MARSLAND, TA MENG, L Computing Science Department University of Alberta Edmonton T6G 2H1 Canada

ISBN: (纸本)3540535047

An analysis of various response times for a proposed network multiprocessor database machine is given in this paper. The insights gained should lead to better database machine designs. Our systems is based on a principle of "shared-nothing" and aims at supporting large database applications by using parallelism. Our research shows that a highly parallel network multiprocessor DBM should be able to solve the I/O, data processing, and data transfer bottlenecks. A key to overcoming these problems is eliminating the irrelevant data retrieved by the query at each processing stage. For this, we consider both software and hardware techniques, not the use of a multiprocessor architecture alone. © Springer-Verlag Berlin Heidelberg 1990.

关键词： INFORMATION processing MULTIPROCESSOR dataBASE MACHINES network data processing PERFORMANCE ANALYSIS BOTTLENECKS

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