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Abnormal behavior detection mechanism using deep learning for zero-trust security infrastructure

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Abstract

As ICT technology has developed, work has become possible in a variety of locations and working from home has become more active. Intranet-type information network access was physically connected within the corporate building. Currently, access to the Internet is possible from outside, regardless of geographical location. Because of this, in addition to strengthening internal security, numerous studies are being conducted on external threat factors, user authentication, and data security. However, sophisticated attacks require security technologies such as enhanced network access control and strict user authentication. In this study, we propose an Abnormal Behavior Detection Mechanism (ABDM) that analyzes packets for various purposes for external access and determines abnormal behavior using a zero-trust perspective. ABDM approached users, systems, and time series to analyze packets and determine abnormal behavior. As a result, an accuracy of approximately 93% for abnormal behavior was measured.

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Acknowledgements

This paper was supported by Wonkwang University in 2023.

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Authors and Affiliations

  1. Department of Software Engineering, Baewha Women’s University, Seoul, Republic of Korea

    Hyun-Woo Kim

  2. Division of Liberal Arts, Wonkwang University, Iksan, Jeonbuk, Republic of Korea

    Eun-Ha Song

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  1. Hyun-Woo Kim
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Correspondence to Eun-Ha Song.

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Kim, HW., Song, EH. Abnormal behavior detection mechanism using deep learning for zero-trust security infrastructure. Int. j. inf. tecnol. 16, 5091–5097 (2024). https://doi.org/10.1007/s41870-024-02110-7

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