5G and beyond; Deep learning; Federated learning; IDS; NON-IID; 5g and beyond; Centralised; Collaborative approach; Data distribution; Intrusion Detection Systems; Learning models; NON-identically distributed; Service provider; Computer Science (all); Law; General Computer Science
Résumé :
[en] 5G's service providers now leverage Deep Learning (DL) to automate their network slice management, provisioning, and security. To this end, each slice owner contributes data to feed a common dataset used to train centralized learning models. However, this method raises privacy considerations that prevent its usage. Therefore, Federated learning (FL), a collaborative approach that ensures data privacy, is being investigated while striving toward the same performance as centralized learning. As 5G and beyond services are so diverse, the local slice's data is not intended to reflect the entire data distribution. Thus, local data of slices are Non-Independently and non-Identically distributed (Non-IID), posing a challenge for FL-based models. In this paper, we investigate the use of FL to secure network slices and detect potential attacks. For that purpose, we first propose an architecture for deploying intrusion detection systems (IDSs) in 5G and beyond networks. Next, we thoroughly evaluate the latest state-of-art FL algorithms, including FedAvg, FedProx, FedPer, and SCAFFOLD, in the context of Independently and Identically Distributed (IID) and Non-IID data distributions. We compare these FL models to centralized and local DL models. We find that SCAFFOLD outperforms all the other FL algorithms and ensures a stable learning loss convergence, a promising finding that strengthens the case for leveraging FL in IDS development. Nevertheless, none of the FL models could achieve the centralized model's performance in Non-IID scenarios.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Djaidja, Taki Eddine Toufik ; DRIVE Laboratory EA1859, University of Burgundy, Nevers, France
Brik, Bouziane ; Computer Science Department, College of Computing and Informatics, Sharjah University, United Arab Emirates
BOUALOUACHE, Abdelwahab ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Senouci, Sidi Mohammed; DRIVE Laboratory EA1859, University of Burgundy, Nevers, France
Ghamri-Doudane, Yacine; L3I Laboratory, Univ. La Rochelle, France
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Federated learning for 5G and beyond, a blessing and a curse- an experimental study on intrusion detection systems
FNR14891397 - Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks, 2020 (01/04/2021-31/03/2024) - Thomas Engel
Organisme subsidiant :
Agence Nationale de la Recherche Fonds National de la Recherche Luxembourg
Subventionnement (détails) :
This work was supported by the 5G-INSIGHT bilateral project (ID: 14891397 ) / ( ANR-20-CE25-0015-16 ), funded by the Luxembourg National Research Fund (FNR), and by the French National Research Agency (ANR).
Chen, Z., Lv, N., Liu, P., Fang, Y., Chen, K., Pan, W., Intrusion detection for wireless edge networks based on federated learning. IEEE Access 8 (2020), 217463–217472, 10.1109/ACCESS.2020.3041793.
Chowdhury, M.Z., Shahjalal, M., Ahmed, S., Jang, Y.M., 6g wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open J. Commun. Soc. 1 (2020), 957–975, 10.1109/OJCOMS.2020.3010270.
Dogra, A., Jha, R.K., Jain, S., A survey on beyond 5g network with the advent of 6g: architecture and emerging technologies. IEEE Access 9 (2021), 67512–67547, 10.1109/ACCESS.2020.3031234.
Fan, Y., Li, Y., Zhan, M., Cui, H., Zhang, Y., Iotdefender: a federated transfer learning intrusion detection framework for 5g iot. 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), 2020, 88–95, 10.1109/BigDataSE50710.2020.00020.
Hofstede, R., Čeleda, P., Trammell, B., Drago, I., Sadre, R., Sperotto, A., Pras, A., Flow monitoring explained: from packet capture to data analysis with netflow and ipfix. IEEE Commun. Surv. Tutor. 16 (2014), 2037–2064, 10.1109/COMST.2014.2321898.
Kang, H., Ahn, D.H., Lee, G.M., Yoo, J.D., Park, K.H., Kim, H.K., Iot network intrusion dataset. https://doi.org/10.21227/q70p-q449, 2019.
Lavaur, L., Pahl, M.O., Busnel, Y., Autrel, F., The evolution of federated learning-based intrusion detection and mitigation: a survey. IEEE Trans. Netw. Serv. Manag. 19:3 (2022), 2309–2332, 10.1109/TNSM.2022.3177512.
Li, T., Sahu, A.K., Talwalkar, A., Smith, V., Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37 (2020), 50–60, 10.1109/MSP.2020.2975749.
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V., Federated optimization in heterogeneous networks. https://arxiv.org/abs/1812.06127, 2018 https://doi.org/10.48550/ARXIV.1812.06127.
Ma, X., Zhu, J., Lin, Z., Chen, S., Qin, Y., A state-of-the-art survey on solving non-iid data in federated learning. Future Gener. Comput. Syst. 135 (2022), 244–258, 10.1016/j.future.2022.05.003 https://www.sciencedirect.com/science/article/pii/S0167739X22001686.
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.y., Communication-efficient learning of deep networks from decentralized data. https://arxiv.org/abs/1602.05629, 2016 https://doi.org/10.48550/ARXIV.1602.05629.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., Elovici, Y., N-baiot: Network-Based Detection of Iot Botnet Attacks Using Deep Autoencoders. 2018.
Mothukuri, V., Khare, P., Parizi, R.M., Pouriyeh, S., Dehghantanha, A., Srivastava, G., Federated-learning-based anomaly detection for iot security attacks. IEEE Int. Things J. 9 (2022), 2545–2554, 10.1109/JIOT.2021.3077803.
Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G., A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115 (2021), 619–640, 10.1016/j.future.2020.10.007 https://www.sciencedirect.com/science/article/pii/S0167739X20329848.
Naser, M.Z., Alavi, A.H., Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences. Architecture, Structures and Construction 3 (2021), 499–517, 10.1007/s44150-021-00015-8.
Rahman, K.M.J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K.E., Islam, A.K.M.M., Mukta, M.S.H., Islam, A.K.M.N., Challenges, applications and design aspects of federated learning: a survey. IEEE Access 9 (2021), 124682–124700, 10.1109/ACCESS.2021.3111118.
Rahman, S.A., Tout, H., Talhi, C., Mourad, A., Internet of things intrusion detection: centralized, on-device, or federated learning?. IEEE Netw. 34 (2020), 310–317, 10.1109/MNET.011.2000286.
Sharafaldin, I., Habibi Lashkari, A., Ghorbani, A., Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 2018, 108–116 https://doi.org/10.5220/0006639801080116.
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A., A detailed analysis of the kdd cup 99 data set. 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, 1–6, 10.1109/CISDA.2009.5356528.
Wahab, O.A., Mourad, A., Otrok, H., Taleb, T., Federated machine learning: survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Commun. Surv. Tutor. 23 (2021), 1342–1397, 10.1109/COMST.2021.3058573.
Yuan, Y., Gehrmann, C., Sternby, J., Barriga, L., Insight of anomaly detection with nwdaf in 5g. 2022 International Conference on Computer, Information and Telecommunication Systems (CITS), 2022, 1–6, 10.1109/CITS55221.2022.9832914.
Zhang, S., An overview of network slicing for 5g. IEEE Wirel. Commun. 26 (2019), 111–117, 10.1109/MWC.2019.1800234.
Zhang, T., He, C., Ma, T., Gao, L., Ma, M., Avestimehr, S., Federated learning for Internet of things. Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021, Association for Computing Machinery, New York, NY, USA, 413–419, 10.1145/3485730.3493444.
