CNN-based Application Recognition to Enhance Network Governance for Financial Networks

• Published in: 2024 International Conference on Computer, Information and Telecommunication Systems (CITS) pp. 1 - 5
• Main Authors: Gadhiya, Urmil, Faldu, Preet, Darji, Krisha, Obaidiat, Mohammad S., Gupta, Rajesh, Tanwar, Sudeep
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17-07-2024
• Subjects: Accuracy; CNN-based application recognition; Computer architecture; Deep learning; Network Governance; Network Traffic Classification; Pattern recognition; Streaming media; Telecommunication traffic; Telecommunications
• DOI: 10.1109/CITS61189.2024.10608028

Effective network governance is essential to guarantee optimal performance, security, and resource allocation in the increasing variety of network applications and the growing complexity of network environments. In this study, we used convolutional neural network (CNN)-based application recognition which uses deep learning techniques to identify network applications and classify them accurately. We designed and evaluated a customized CNN architecture for a recognition application, using raw data as input to capture unique patterns and features. We demonstrate the efficiency of our method in accurately identifying the variety of network applications through an experiment performed on real-world network datasets, that includes web browsing, video streaming, file transfer, and more. We then evaluate the performance of our system using various performance evaluating measures, which include accuracy, precision, recall, and F1-score, and then compare it with the traditional methods to emphasize its superiority in accuracy and efficiency. Lastly, our research on this comes up with the advancement of network governance by providing powerful and scalable solutions for application network management which makes a way to improve network performance, security, and resource development.