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MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based Systems

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Abstract

The massive growth in data generation, experienced throughout the current century, has enabled the design of data-driven solutions for various applications. On the other hand, privacy concerns have been raised, especially considering the problems that the leakage of personal data can cause. To address privacy and security issues when dealing with sensitive content, works in the literature have focused on improving protocols for content sharing, primarily by endowing them with anomaly detection modules. However, in Blockchain-based systems, the aggregation of anomaly detection modules to middleware environments is still an under-explored research direction. This paper introduces the Middleware for Anomaly Detection and Content Sharing (MADCS), a new middleware based on a layered structure composed of the application, preprocessing, data analysis and business layers, besides the Blockchain platform. For validation, we built a synthetic dataset of medical prescriptions following an international standard and applied a clustering-based technique for anomaly detection. Experiments demonstrated 85% precision and 78% accuracy in identifying abnormalities in the content-sharing process. The results show that a Blockchain combined with MADCS may contribute to a safer content-sharing network environment.

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Availability of data and materials

all data was generated from the simulations described in the paper, available at https://github.com/alef123vinicius/MADCS.

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Acknowledgements

This research was funded by the Coordination of Improvement of Higher Education Personnel (CAPES), the Sao Paulo Research Foundation (FAPESP, grants 2018/17335-9 and 2021/10921-2), the Center of Mathematical Sciences Applied to Industry (CeMEAI, under FAPESP grant number 2013/07375-0), and the National Council for Scientific and Technological Development (CNPq).

Funding

this research was funded by the Coordination of Improvement of Higher Education Personnel (CAPES), the Sao Paulo Research Foundation (FAPESP, grants 2018/17335-9 and 2021/10921-2), the Center of Mathematical Sciences Applied to Industry (CeMEAI, under FAPESP grant number 2013/07375-0), and the National Council for Scientific and Technological Development (CNPq).

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Authors and Affiliations

  1. Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil

    Alef Vinicius Cardoso e Silva, Caetano Mazzoni Ranieri, Rodolfo Ipolito Meneguette, Rodrigo Dutra Garcia & Jó Ueyama

  2. Flextronics Institute of Technology, Sorocaba, 8087-220, São Paulo, Brazil

    Felipe Taliar Giuntini

  3. Computer Science Institute, Federal University of Amazonas, Manaus, Amazonas, Brazil

    Felipe Taliar Giuntini

  4. School of Computer Science, Queensland University of Technology, 2 George St, Brisbane City, 4000, QLD, Australia

    Gowri Sankar Ramachandran

  5. Viterbi School of Engineering, University of Southern California, Los Angeles, 90007, CA, USA

    Bhaskar Krishnamachari

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  1. Alef Vinicius Cardoso e Silva
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Contributions

AS and FG implemented the system, performed the experiments, analyzed the results and wrote the first draft. CR and RG improved the conceptual design of the research, structured the paper and checked the references. RM, GR and BK provided intellectual contributions to the design and experiments. JU coordinated the methodological aspects of the work. All authors reviewed the manuscript.

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Correspondence to Caetano Mazzoni Ranieri.

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Silva, A.V.C., Giuntini, F.T., Ranieri, C.M. et al. MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based Systems. J Netw Syst Manage 31, 46 (2023). https://doi.org/10.1007/s10922-023-09736-1

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