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27 April 2023 Comparison of the applicability of synergistic models with dense neural networks on the example of mobile device security
J. Vishnevskaya, B. Salyp
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Proceedings Volume 12637, International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023); 126370F (2023) https://doi.org/10.1117/12.2680842
Event: International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023), 2023, Fergana, Uzbekistan
Abstract
The dominant approach in contemporary science and industry applications is deep learning. Deep learning consists of many different architectures, including dense, convolutional and recurrent neural networks. In this article we compare deep learning, particularly dense neural networks (DNNs) with the other approach, synergetic models. As a task to compare the mobile application security was chosen. Two datasets were created, sensor values for human beings interacting with smartphones and malicious bot records using emulators. Deep learning and synergetic models were tasked to distinguish them from each other using binary classification. Both models tackled a problem comparatively well, archiving 99% and 85% accuracy respectively. While the deep learning model performed better, the synergetic model excelled in training speed, versatility and results transparency. Pros and cons of both models were addressed in the results section.
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J. Vishnevskaya and B. Salyp "Comparison of the applicability of synergistic models with dense neural networks on the example of mobile device security", Proc. SPIE 12637, International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023), 126370F (27 April 2023); https://doi.org/10.1117/12.2680842
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KEYWORDS
Neural networks
Data modeling
Deep learning
Information security
Mathematical modeling
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