Abstract
Recently, different deep learning based techniques have been proposed to detect intrusions and anomalies in the information systems. Convolutional neural networks are often used to reveal hidden spatial relations between features, however their application requires certain data preprocessing techniques that transform tabular non-spatial data to matrices. This paper studies different approaches to tabular data transformation to images and analyzes their impact on the efficiency of attack detection including the ability to detect novel and unseen attacks. Experiments are conducted using the CICIDS2017 dataset, which describes network traffic flows as numerical vectors and contains different types of attacks. The conducted research allowed us to conclude on areas of applicability of tabular data transformation to images considering advantages and limitations of such preprocessing.
The research is supported by the grant of Russian Science Foundation #23-11-20024, https://rscf.ru/en/project/23-11-20024/, and St. Petersburg Science Foundation.
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Novikova, E., Bukhtiarov, M., Kotenko, I., Fedorchenko, E., Saenko, I. (2024). Towards Application of the Tabular Data Transformation to Images in the Intrusion Detection Tasks Using Deep Learning Techniques. In: Köhler-Bußmeier, M., Renz, W., Sudeikat, J. (eds) Intelligent Distributed Computing XVI. IDC 2023. Studies in Computational Intelligence, vol 1138. Springer, Cham. https://doi.org/10.1007/978-3-031-60023-4_12
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