Simulation of IIoT-Driven Attack Vectors on 5G Core Networks: Dataset Generation and Machine Learning Based Detection
Prasad, Suranga; Munaweera, Pramod; Hewa, Tharaka; Siriwardhana, Yushan; Ylianttila, Mika (2025-03-31)
ACM
31.03.2025
Prasad, S., Munaweera, P., Hewa, T., Siriwardhana, Y., & Ylinattila, M. (2024). Simulation of iiot-driven attack vectors on 5g core networks: Dataset generation and machine learning based detection. Proceedings of the 14th International Conference on the Internet of Things, 184–187. https://doi.org/10.1145/3703790.3703815
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504092483
https://urn.fi/URN:NBN:fi:oulu-202504092483
Tiivistelmä
Abstract
The emergence of 5G technology has accelerated the development of Industrial Internet of Things (IIoT) applications, enabling a wide range of innovations across multiple industries. However, the integration of 5G and IIoT introduces new security vulnerabilities in core networks due to the vast number of connected devices and the lack of robust security measures in these devices. These vulnerabilities provide intruders with new opportunities to attack the core network. In our research, we demonstrate several types of potential attacks from IoT devices on the core network, collect the attack data into a proper dataset, and implement a Machine Learning (ML) model to detect these threats. The collected data can be used to test different ML models designed to detect intrusions in the core network. The results of this work will contribute to the development of advanced security measures, enhancing the resilience and reliability of 5G infrastructures against emerging cyber threats.
The emergence of 5G technology has accelerated the development of Industrial Internet of Things (IIoT) applications, enabling a wide range of innovations across multiple industries. However, the integration of 5G and IIoT introduces new security vulnerabilities in core networks due to the vast number of connected devices and the lack of robust security measures in these devices. These vulnerabilities provide intruders with new opportunities to attack the core network. In our research, we demonstrate several types of potential attacks from IoT devices on the core network, collect the attack data into a proper dataset, and implement a Machine Learning (ML) model to detect these threats. The collected data can be used to test different ML models designed to detect intrusions in the core network. The results of this work will contribute to the development of advanced security measures, enhancing the resilience and reliability of 5G infrastructures against emerging cyber threats.
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