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Privacy-Preserving User Abnormal Behavior Detection in 5G Networks
Date
2023-01-01
Author
Acir, Berker
Onur, Ertan
Boutaba, Raouf
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Network Data Analytics Function (NWDAF) plays a key role in autonomous network management and security using machine learning (ML) techniques. However, not all ML methods such as convolutional neural networks, deep language models can be deployed in the core network due to their high computational requirements. Besides, telcos may prefer handing over ML analytics to experienced third parties instead of implementing themselves. In this paper, we show that delay-tolerant tasks of NWDAF can be offloaded to public third party cloud providers while preserving user and data privacy by employing homomorphic encryption (HE). We focus on the problem of abnormal behaviour detection for a group of user equipment (UE) and train various DL models with plaintext data. We demonstrate that inferencing on encrypted data is as accurate and precise as plaintext inferencing and we quantify the performance degradation in terms of running times. We believe that offloading NWDAF tasks to the cloud allows telcos to focus on expanding their core capabilities, and reduce capital and operational expenditures.
Subject Keywords
5G
,
Deep Learning
,
Homomorphic Encryption
,
NWDAF
,
Privacy-Preserving
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179007527&origin=inward
https://hdl.handle.net/11511/106961
DOI
https://doi.org/10.1109/blackseacom58138.2023.10299735
Conference Name
2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023
Collections
Department of Computer Engineering, Conference / Seminar
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BibTeX
B. Acir, E. Onur, and R. Boutaba, “Privacy-Preserving User Abnormal Behavior Detection in 5G Networks,” presented at the 2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023, İstanbul, Türkiye, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179007527&origin=inward.