Reducing the Dimensionality of Data Arrays Using Multi-Layer Autoencoders in the Task of Classifying Mobile Applications

The problem of reducing the dimension of the initial data arrays to improve the efficiency of mobile application traffic processing is considered. The relevance of the study is due to the need to optimize the volume of transmitted and stored data when working in conditions of limited computing resources, as well as to increase the speed and quality of analytical operations. To solve this problem, multi-layer autoencoders are used, capable of forming compact representations of the source data with minimal losses in their informativeness. The approach is based on the idea of training neural network models that extract the most significant features from the source arrays and are able to restore them with a given level of accuracy. Methods used. During the experiments, various architectures of multilayer autocoders were used, differing in the number of layers and dimensions of hidden representations. The research was conducted on real data sets collected from mobile applications with a wide range of functionality. The analysis was carried out by varying the internal parameters of the networks and evaluating the results through an integral statistical indicator reflecting the degree of compression. This indicator allows you to identify how much the spread of attributes changes when passing data through the autoencoder.

Results. To evaluate the filtering properties of multilayer autoencoders, an integral compression indicator is proposed that characterizes the change in the spread of attributes of mobile applications when passing them through an autoencoder of a given structure. The indicator is calculated as the ratio of the standard deviation of the attributes at the input and at the output, which allows you to assess the degree of data compression and the degree of information preservation after processing. It is shown that an increase in the integral compression index indicates a more significant compression of the initial data. It was found that filtering is practically independent of the type of application and lies within 10-20 % for three-layer autoencoders, whereas for five-layer auto-encoders, preference is given to encoders with a minimum dimension of the inner layer. The main novelty of the work lies in the development of an integral statistical indicator that not only reflects the degree of compression of mobile application data, but also takes into account the preservation of the original information structure. Unlike existing approaches, this indicator allows for a systematic comparison of various architectures of autoencoders, taking into account not only the reduction in dimension, but also the quality of recovery of the original information. This creates the basis for a more objective assessment of the effectiveness of multilayer autoencoders in specific application conditions.

Practical significance. The proposed methodology may be useful for developers and researchers working on optimizing systems for collecting, storing and processing mobile application data. In conditions of limited computing resources, which are typical for mobile devices and embedded systems, the use of multilayer autoencoders aimed at achieving a given balance between compression and preservation of information provides a significant reduction in the volume of transmitted data. The results of the study can be implemented into existing analytical platforms, monitoring systems and classification of mobile applications.

1. Goodfellow I., Bengio Y., Courville A. Deep Learning. The MIT Press, 2016. 800 p.

2. Hinton G.E., Osindero S., Teh Y.W. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation. 2006;18(7): 1527–1554. DOI:10.1162/neco.2006.18.7.1527

3. Salakhutdinov R., Hinton G.E. Deep Boltzmann Machines. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (Clearwater Beach, USA). Proceedings of Machine Learning Research, vol.5. 2009. p.448–455.

4. Kuzmina М.G. Multilayered autoencoders in problems of hyperspectral image analysis and processing. Preprint M.V. Keldysh IAM. 2021;28:21. DOI:10.20948/prepr-2021-28

5. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal. 1991;37(2) 233‒243. DOI:10.1002/aic.690370209

6. Bengio Y., Lamblin P., Popovici D., Larochelle H. Greedy Layer-Wise Training of Deep Networks. In: Advances in Neural Information Processing Systems (B. Schölkopf, J. Platt, T. Hoffman (eds.). Cambridge; 2007. p.153–160.

7. Windrim L., Ramakrishnan R., Melkumyan A., Murphy R.J., Chlingaryan A. Unsupervised feature-learning for hyper-spectral data with autoencoders. Remote Sensing. 2019;11(7):864. DOI:10.3390/rs11070864

8. Sheluhin O.I. Barkov V.V. Simonyan A.G. Concept Drift Detection in Mobile Applications Classification Using Autoencoders. H&ES Research. 2023;15(3):20–29. (in Russ.) DOI:10.36724/2409-5419-2023-15-3-20-29. EDN:KBWOOG

9. Sheluhin O.I. Barkov V.V. Matorin F.A. Improving the classification of illegal and unwanted applications under back-ground traffic conditions using autoencoders. Bulletin of the St. Petersburg State University of Technology and Design: Series 1. Natural and technical sciences. 2023;3:159–165 (in Russ.) DOI:10.46418/2079-8199_2023_3_25. EDN:RLBDBM

10. Ososkov G., Goncharov P. Shallow and deep learning for image classification. Optical Memory and Neural Networks. 2017;26(4):221–248. DOI:10.3103/S1060992X1704004X

11. Sheluhin O.I., Zegzhda D.P., Rakovsk, D.I., Samari, N.N., Aleksandrova E.B. Intelligent Technologies of Information Security. Moscow: Goryachaya Liniya – Telecom Publ.; 2023. 384 p. (in Russ.)

12. Sheluhin O.I., Erokhin S.D., Barkov V.V. Creation of a Network Traffic Database for Automating the Classification of Mobile Applications under the Android Operating System. Neurocomputers: Development, Application. 2019;1:40–51. (in Russ.) DOI:10.18127/j19998554-201901-06. EDN:BDDXDT

13. Sheluhin O.I., Barkov V.V. Experimental Studies and Creation of a Network Traffic Database of Mobile Devices under the Android Operating System. Fundamental Problems of Radio Electronic Instrument Engineeringю 2018;18(4):1011–1017. (in Russ.) EDN:ZABZMT