Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy
DOI:
https://doi.org/10.46243/jst.2025.v10.i02.pp95-107Keywords:
Blockchain, Federated Learning,, Anomaly Detection,, Differential Privacy,, Cybersecurity, Isolation Forest,, Variational AutoencodersAbstract
The complexity of the cyber threats dictates the need for strong, privacy-preserving
mechanisms for anomaly detection. This paper introduces a new framework called BAFL, an
integration of Isolation Forest and Variational Autoencoders combined with Differential
Privacy, for safe and scalable solutions in cybersecurity applications. Federated Learning
allows distributed training across numerous clients without exposure of sensitive information,
while the blockchain technology introduces trust and integrity in model updates.
References
Tao, X., Peng, Y., Zhao, F., Zhao, P., & Wang, Y. (2018). A parallel algorithm for network traffic anomaly detection based on Isolation Forest. International Journal of Distributed Sensor Networks, 14(11), 1550147718814471
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained anomaly detection models for federated learning: An intrusion detection case study. Applied Sciences, 8(12), 2663
Abdulhammed, R., Faezipour, M., Abuzneid, A., & AbuMallouh, A. (2018). Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE sensors letters, 3(1), 1-4
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018). A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017
Chen, Q., Xiang, C., Xue, M., Li, B., Borisov, N., Kaarfar, D., & Zhu, H. (2018). Differentially private data generative models. arXiv preprint arXiv:1812.02274
Alotaibi, A. (2018). Wisdom of the machines: federated learning using OPAL (Doctoral dissertation, Massachusetts Institute of Technology)
Shayan, M., Fung, C., Yoon, C. J., & Beschastnikh, I. (2018). Biscotti: A ledger for private and secure peer-to-peer machine learning. arXiv preprint arXiv:1811.09904
Ning, X., Zheng, Y., Jiang, Z., Wang, Y., Yang, H., & Huang, J. (2018). A Bayesian nonparametric topic model with variational auto-encoders
Zhang, Y. (2018). Deep generative model for multi-class imbalanced learning
Narayana, P. (2018). A prototype to detect anomalies using machine learning algorithms and deep neural network. In Computational Vision and Bio Inspired Computing (pp. 1084- 1094). Springer International Publishing
Hynes, N., Dao, D., Yan, D., Cheng, R., & Song, D. (2018). A demonstration of sterling: a privacypreserving data marketplace. Proceedings of the VLDB Endowment, 11(12), 2086-2089
Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557
Zhou, W., Li, Y., Chen, S., & Ding, B. (2018, October). Real-time data processing architecture for multi-robots based on differential federated learning. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/ IOP/SCI) (pp. 462-471). IEEE
Hartmann, F. (2018). Federated learning. Freie Universität Berlin
McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018). A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210. DOI:https://doi.org/10.46243/jst.2025.v10.i02.pp95- 10751 Durga Praveen Devi, Naga Sushma Allur, Koteswararao Dondapati, Himabindu Chetlapalli, Sharadha Kodadi, Aravindhan Kurunthachalam: Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy
Zhang, T., & Zhu, Q. (2017). Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning. IEEE Transactions on Information Forensics and Security, 12(1), 172–
Harder, F., Köhler, J., Welling, M., & Park, M. (2018). DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Malomo, O., Rawat, D. B., & Garuba, M. (2018). Next-generation cybersecurity through a blockchain-enabled federated cloud framework. The Journal of Supercomputing, 74(10), 5099–5126
https://www.kaggle.com/datasets/ ameerhamza123/intrusion-detection-dataset
Published
How to Cite
Issue
Section
