TY - GEN
T1 - Machine Learning 5G Attack Detection in Programmable Logic
AU - Coldwell, Cooper
AU - Conger, Denver
AU - Goodell, Edward
AU - Jacobson, Brendan
AU - Petersen, Bryton
AU - Spencer, Damon
AU - Anderson, Matthew W
AU - Sgambati, Matthew
N1 - Funding Information:
VII. ACKNOWLEDGEMENTS This research made use of the resources of the High Performance Computing Center at Idaho National Laboratory, which is supported by the Office of Nuclear Energy of the U.S. Department of Energy and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517. We acknowledge Christopher Becker, Jessie Cooper, and the Wireless Security Institute from Idaho National Laboratory for their technical assistance and review. This manuscript has been authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.
Publisher Copyright:
© 2022 IEEE.
PY - 2023
Y1 - 2023
N2 - Machine learning-assisted network security may significantly contribute to securing 5G components. However, machine learning network security inference generally requires tens to hundreds of milliseconds, thereby introducing significant latency in 5G operations. The inference latency can be reduced by deploying the machine learning model to programmable logic in a field programmable gate array at the cost of a small loss in accuracy. In order to quantify this loss, as well as to establish baseline performance inference latency for programmable logic implementations, this work explores an autoencoder and a beta-variational autoencoder deployed on two different field programmable gate array evaluation boards and compares accuracy and performance against an NVIDIA A100 graphics processing unit implementation. A publicly available 5G dataset containing 10 types of attacks along with normal traffic is introduced as part of the evaluation.
AB - Machine learning-assisted network security may significantly contribute to securing 5G components. However, machine learning network security inference generally requires tens to hundreds of milliseconds, thereby introducing significant latency in 5G operations. The inference latency can be reduced by deploying the machine learning model to programmable logic in a field programmable gate array at the cost of a small loss in accuracy. In order to quantify this loss, as well as to establish baseline performance inference latency for programmable logic implementations, this work explores an autoencoder and a beta-variational autoencoder deployed on two different field programmable gate array evaluation boards and compares accuracy and performance against an NVIDIA A100 graphics processing unit implementation. A publicly available 5G dataset containing 10 types of attacks along with normal traffic is introduced as part of the evaluation.
UR - https://www.scopus.com/pages/publications/85146873076
UR - https://www.mendeley.com/catalogue/eabf8e1a-d554-31f2-aad4-42cbf25e08a6/
U2 - 10.1109/GCWkshps56602.2022.10008647
DO - 10.1109/GCWkshps56602.2022.10008647
M3 - Conference contribution
AN - SCOPUS:85146873076
T3 - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
SP - 1365
EP - 1370
BT - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Globecom Workshops, GLOBECOM Workshop 2022
Y2 - 4 December 2022 through 8 December 2022
ER -