TY - GEN
T1 - Robust Deep Learning against Corrupted Data in Cognitive Autonomous Networks
AU - Kajo, Marton
AU - Schnellbach, Janik
AU - Mwanje, Stephen S.
AU - Carle, Georg
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Neural-net-based deep learning algorithms are starting to be utilized in many network functions. Deep neural nets are traditionally not resistant against missing or corrupted inputs, a scenario which is likely to happen in mobile networks. If the data corruption does not stem from malicious intent, the task of reconstructing missing inputs is called imputation. In this paper, we discuss how such imputation methods could be utilized in network functions, to make the network robust against non-adversarial data corruption. We propose an integrated approach, where the imputation is undertaken by the same model which implements the machine learning task in the network function. We evaluate state-of-the-art imputation methods and our integrated imputation thoroughly, using data generated in a mobile network simulator. Our results show excellent performance with the integrated imputation, but also raises some questions with regards to how deep-learning-based network functions should be used in such scenarios.
AB - Neural-net-based deep learning algorithms are starting to be utilized in many network functions. Deep neural nets are traditionally not resistant against missing or corrupted inputs, a scenario which is likely to happen in mobile networks. If the data corruption does not stem from malicious intent, the task of reconstructing missing inputs is called imputation. In this paper, we discuss how such imputation methods could be utilized in network functions, to make the network robust against non-adversarial data corruption. We propose an integrated approach, where the imputation is undertaken by the same model which implements the machine learning task in the network function. We evaluate state-of-the-art imputation methods and our integrated imputation thoroughly, using data generated in a mobile network simulator. Our results show excellent performance with the integrated imputation, but also raises some questions with regards to how deep-learning-based network functions should be used in such scenarios.
KW - Cognitive Autonomous Networks
KW - Deep Learning
KW - Imputation
KW - Network Management Automation
UR - http://www.scopus.com/inward/record.url?scp=85133207733&partnerID=8YFLogxK
U2 - 10.1109/NOMS54207.2022.9789774
DO - 10.1109/NOMS54207.2022.9789774
M3 - Conference contribution
AN - SCOPUS:85133207733
T3 - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022
BT - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022
A2 - Varga, Pal
A2 - Granville, Lisandro Zambenedetti
A2 - Galis, Alex
A2 - Godor, Istvan
A2 - Limam, Noura
A2 - Chemouil, Prosper
A2 - Francois, Jerome
A2 - Pahl, Marc-Oliver
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Y2 - 25 April 2022 through 29 April 2022
ER -