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    "title": "Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy",
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                "doi": "10.3390/app8122663",
                "unstructured": "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"
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                "doi": "10.1109/lsens.2018.2879990",
                "unstructured": "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"
            },
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                "key": "ref4",
                "doi": "10.1038/s42256-021-00337-8",
                "unstructured": "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"
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                "unstructured": "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"
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                "unstructured": "Alotaibi, A. (2018). Wisdom of the machines: federated learning using OPAL (Doctoral dissertation, Massachusetts Institute of Technology)"
            },
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                "key": "ref7",
                "doi": "10.1109/tpds.2020.3044223",
                "unstructured": "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"
            },
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                "key": "ref8",
                "unstructured": "Ning, X., Zheng, Y., Jiang, Z., Wang, Y., Yang, H., & Huang, J. (2018). A Bayesian nonparametric topic model with variational auto-encoders"
            },
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                "key": "ref9",
                "doi": "10.23860/thesis-zhang-yazhou-2018",
                "unstructured": "Zhang, Y. (2018). Deep generative model for multi-class imbalanced learning"
            },
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                "key": "ref10",
                "doi": "10.1007/978-3-319-71767-8_93",
                "unstructured": "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"
            },
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                "key": "ref11",
                "doi": "10.14778/3229863.3236266",
                "unstructured": "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"
            },
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                "key": "ref12",
                "unstructured": "Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557"
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                "key": "ref13",
                "doi": "10.1109/smartworld.2018.00106",
                "unstructured": "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"
            },
            {
                "key": "ref14",
                "unstructured": "Hartmann, F. (2018). Federated learning. Freie Universität Berlin"
            },
            {
                "key": "ref15",
                "unstructured": "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"
            },
            {
                "key": "ref16",
                "doi": "10.1109/tifs.2016.2607691",
                "unstructured": "Zhang, T., & Zhu, Q. (2017). Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning. IEEE Transactions on Information Forensics and Security, 12(1), 172–"
            },
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                "unstructured": "Harder, F., Köhler, J., Welling, M., & Park, M. (2018). DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning"
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                "unstructured": "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"
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                "unstructured": "https://www.kaggle.com/datasets/ ameerhamza123/intrusion-detection-dataset"
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