CCF Based System Framework In Federated Learning Against Data Poisoning Attacks

Ibrahim M. Ahmed; Manar Younis Kashmoola

doi:10.6180/jase.202307_26(7).0008

CCF Based System Framework In Federated Learning Against Data Poisoning Attacks

Computer Science and Information Engineering

Ibrahim M. Ahmed This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Manar Younis Kashmoola²

¹College of Science, University of Mosul, Mosul, Iraq
²College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Received: August 17, 2022
Accepted: September 4, 2022
Publication Date: October 14, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202307_26(7).0008

ABSTRACT

Nowadays, smart systems attract a lot of attention as several smart applications are growing. Distributed machine learning such as federated learning has an essential role in smart systems including 6G applications. The main issues that face federated learning (F.L.) are security and performance, which are could be affected by the poisoning attack models. One of the most common poisoning attacks is an impersonation attack, such Sybil attack. This paper proposes a new framework that increases the security of federated learning against Sybil poisoning attacks. The proposed framework which is called FED_CCF, creates a hybrid environment using federate learning with Microsoft CCF (Confidential Consortium Framework). It provides a secure and reliable environment that misleads attackers targeting federated learning. The MNIST dataset is used to investigate the performance of F.L. model with FED_CCF in terms of accuracy. The F.L. model is evaluated by exploiting the MNIST dataset and 30% of malicious devices that use the Sybil attack. The experimental results show that F.L. system implementing FED_CCF outperforms Vanilla F.L. in terms of accuracy, where the former acquired approximately 95.2 % compared to the latter, which only obtains 2.55% employing Sybil poisoning attack.

Keywords: Data poisoning; Federated learning; CCF; Sybil poisoning attacks

REFERENCES

[1] A. A. Laghari, A. K. Jumani, and R. A. Laghari, (2021) “Review and state of art of fog computing" Archives of Computational Methods in Engineering 28(5): 3631–3643. DOI: doi.org/10.1007/s11831-020-09517-y.
[2] A. A. Laghari, K.Wu, R. A. Laghari, M. Ali, and A. A. Khan, (2021) “A review and state of art of Internet of Things (IoT)" Archives of Computational Methods in Engineering: 1–19. DOI: doi.org/10.1007/s11831-021-09622-6.
[3] M. B. Janjua, A. E. Duranay, and H. Arslan, (2020) “Role of wireless communication in healthcare system to cater disaster situations under 6G vision" Frontiers in Communications and Networks 1: 610879. DOI: doi.org/10.3389/frcmn.2020.610879.
[4] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, (2020) “Federated optimization in heterogeneous networks" Proceedings of Machine Learning and Systems 2: 429–450.
[5] X. Zhang and X. Luo, (2020) “Exploiting defenses against GAN-based feature inference attacks in federated learning" arXiv preprint arXiv:2004.12571: DOI: doi.org/10.48550/arXiv.2004.12571..
[6] L. Lyu, H. Yu, and Q. Yang, (2020) “Threats to federated learning: A survey" arXiv preprint arXiv:2003.02133: DOI: doi.org/10.48550/arXiv.2003.02133.
[7] J. Huang, R. Talbi, Z. Zhao, S. Boucchenak, L. Y. Chen, and S. Roos. “An exploratory analysis on users’ contributions in federated learning”. In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPSISA). IEEE. 2020, 20–29. DOI: doi.org/10.1109/TPSISA50397.2020.00014.
[8] Y. Liu, X. Yuan, R. Zhao, Y. Zheng, and Y. Zheng, (2020) “Rc-ssfl: Towards robust and communication efficient semi-supervised federated learning system" arXiv preprint arXiv:2012.04432: DOI: doi.org/10.48550/arXiv.2012.04432.
[9] R. Nazir, K. Kumar, S. David, M. Ali, et al., (2021) “Survey on wireless network security" Archives of Computational Methods in Engineering: 1–20. DOI: doi.org/10.1007/s11831-021-09631-5.
[10] Z. A. Hamed, I. M. Ahmed, and S. Y. Ameen, (2020) “Protecting windows OS against local threats without using antivirus" relation 29(12s): 64–70. DOI: doi.org/10.3389/frcmn.2020.610879.
[11] I. M. Ahmed and M. Y. Kashmoola. “Threats on Machine Learning Technique by Data Poisoning Attack: A Survey”. In: International Conference on Advances in Cyber Security. Springer. 2021, 586–600. DOI: doi.org/10.1007/978-981-16-8059-5_36.
[12] M. Waqas, K. Kumar, A. A. Laghari, U. Saeed, M. M. Rind, A. A. Shaikh, F. Hussain, A. Rai, and A. Q. Qazi, (2022) “Botnet attack detection in Internet of Things devices over cloud environment via machine learning" Concurrency and Computation: Practice and Experience 34(4): e6662. DOI: doi.org/10.1002/cpe.6662.
[13] M. Russinovich, E. Ashton, C. Avanessians, M. Castro, A. Chamayou, S. Clebsch, M. Costa, C. Fournet, M. Kerner, S. Krishna, et al., (2019) “CCF: A framework for building confidential verifiable replicated services" Technical report, Microsoft Research and Microsoft Azure:
[14] S. V. A. Amanuel and S. Y. A. Ameen, (2021) “Deviceto-device communication for 5G security: a review" Journal of Information Technology and Informatics 1(1): 26–31.
[15] E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov. “How to backdoor federated learning”. In: International Conference on Artificial Intelligence and Statistics. PMLR. 2020, 2938–2948.
[16] Y. Zhao, J. Chen, J. Zhang, D. Wu, M. Blumenstein, and S. Yu, (2022) “Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks" Concurrency and Computation: Practice and Experience 34(7): e5906. DOI: doi.org/10.1002/cpe.5906.
[17] M. Goldblum, D. Tsipras, C. Xie, X. Chen, A. Schwarzschild, D. Song, A. Madry, B. Li, and T. Goldstein, (2022) “Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses" IEEE Transactions on Pattern Analysis and Machine Intelligence: DOI: doi.org/10.1109/TPAMI.2022.3162397.
[18] Y. Jiang, Y. Li, Y. Zhou, and X. Zheng. “Sybil Attacks and Defense on Differential Privacy based Federated Learning”. In: 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE. 2021, 355–362. DOI: doi.org/10.1109/TrustCom53373.2021.00062.
[19] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, (2016) “Infogan: Interpretable representation learning by information maximizing generative adversarial nets" Advances in neural information processing systems 29:
[20] G. Sun, Y. Cong, J. Dong, Q. Wang, L. Lyu, and J. Liu, (2021) “Data poisoning attacks on federated machine learning" IEEE Internet of Things Journal: DOI: doi.org/10.1109/JIOT.2021.3128646.
[21] Y. Wang, P. Mianjy, and R. Arora. “Robust learning for data poisoning attacks”. In: International Conference on Machine Learning. PMLR. 2021, 10859–10869.
[22] V. Tolpegin, S. Truex, M. E. Gursoy, and L. Liu. “Data poisoning attacks against federated learning systems”. In: European Symposium on Research in Computer Security. Springer. 2020, 480–501. DOI: doi.org/10.1007/978-3-030-58951-6_24.
[23] M. Fang, X. Cao, J. Jia, and N. Gong. “Local model poisoning attacks to {Byzantine-Robust} federated learning”. In: 29th USENIX Security Symposium (USENIX Security 20). 2020, 1605–1622.
[24] C. Fung, C. J. Yoon, and I. Beschastnikh, (2018) “Mitigating sybils in federated learning poisoning" arXiv preprint arXiv:1808.04866: DOI: https://doi.org/10.48550/arXiv.1808.04866.
[25] A. N. Bhagoji, S. Chakraborty, P. Mittal, and S. Calo. “Model poisoning attacks in federated learning”. In: Proc. Workshop Secur. Mach. Learn.(SecML) 32nd Conf. Neural Inf. Process. Syst.(NeurIPS). 2018, 1–23.
[26] Z. Chen, P. Tian, W. Liao, and W. Yu, (2021) “Towards multi-party targeted model poisoning attacks against federated learning systems" High-Confidence Computing 1(1): 100002. DOI: doi.org/10.1016/j.hcc.2021.100002..
[27] C. Zhou, Y. Sun, D.Wang, and Q. Gao, (2022) “Fed-Fi: Federated Learning Malicious Model Detection Method Based on Feature Importance" Security and Communication Networks 2022: DOI: doi.org/10.1155/2022/7268347.
[28] D. Cao, S. Chang, Z. Lin, G. Liu, and D. Sun. “Understanding distributed poisoning attack in federated learning”. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). IEEE. 2019, 233–239.
[29] H. Hosseini, H. Park, S. Yun, C. Louizos, J. Soriaga, and M. Welling. “Federated Learning of User Verification Models Without Sharing Embeddings”. In: International Conference on Machine Learning. PMLR. 2021, 4328–4336.
[30] X. Li, Z. Qu, S. Zhao, B. Tang, Z. Lu, and Y. Liu, (2021) “Lomar: A local defense against poisoning attack on federated learning" IEEE Transactions on Dependable and Secure Computing: DOI: doi.org/10.1109/TDSC.2021.3135422.
[31] A. Panda, S. Mahloujifar, A. N. Bhagoji, S. Chakraborty, and P. Mittal. “SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification”. In: International Conference on Artificial Intelligence and Statistics. PMLR. 2022, 7587–7624.
[32] X. Cao and N. Z. Gong. “MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 3396–3404. DOI: doi.org/10.48550/arXiv.2203.08669..
[33] A. Shamis, P. Pietzuch, M. Castro, E. Ashton, A. Chamayou, S. Clebsch, A. Delignat-Lavaud, C. Fournet, M. Kerner, J. Maffre, et al., (2021) “PAC: Practical accountability for CCF" arXiv preprint arXiv:2105.13116: DOI: doi.org/10.48550/arXiv.2105.13116.
[34] A. Moghimi, G. Irazoqui, and T. Eisenbarth. “Cachezoom: How SGX amplifies the power of cache attacks”. In: International Conference on Cryptographic Hardware and Embedded Systems. Springer. 2017, 69–90.
[35] H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, H. Möllering, T. D. Nguyen, P. Rieger, A.-R. Sadeghi, T. Schneider, H. Yalame, et al. “SAFELearn: secure aggregation for private federated learning”. In: 2021 IEEE Security and Privacy Workshops (SPW). IEEE. 2021, 56–62. DOI: doi.org/10.1109/SPW53761.2021.00017..
[36] W. Huang, T. Li, D. Wang, S. Du, J. Zhang, and T. Huang, (2022) “Fairness and accuracy in horizontal federated learning" Information Sciences 589: 170–185. DOI: doi.org/10.1016/j.ins.2021.12.102.
[37] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. “Communication-efficient learning of deep networks from decentralized data”. In: Artificial intelligence and statistics. PMLR. 2017, 1273–1282.
[38] S. M. S. A. Abdullah, S. Y. A. Ameen, M. A. Sadeeq, and S. Zeebaree, (2021) “Multimodal emotion recognition using deep learning" Journal of Applied Science and Technology Trends 2(02): 52–58. DOI: doi.org/10.38094/jastt20291.