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Enhancing blockchain scalability and security: the early fraud detection (EFD) framework for optimistic rollups

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Abstract

Blockchain is an emerging technology that improves efficiency, transparency, and security in applications such as fintech, smart cities, healthcare, etc. However, blockchain technology faces scalability issues as the volume of transactions grows. One solution to enhance the scalability is offloading transactions outside the main blockchain layer using the Optimistic Rollup. In this context, we propose the Early Fraud Detection (EFD) framework that utilizes Optimistic Rollups and incorporates early fraud proofs by applying Bloom–Merkle trees that aim to reduce the challenger’s verification time and cost. The EFD framework has been tested using the Ethereum Mainnet Test Network and developed with Solidity. It demonstrates that the proposed EFD framework reduces the total cost to users by 25%. Moreover, it is robust against security threats, including Double Spending, Sybil, and Denial-of-Service (DOS) attacks.

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Acknowledgements

I am writing to submit our manuscript entitled, “Enhancing Blockchain Scalability and Security: The Early Fraud Detection (EFD) Framework for Optimistic Rollup”, to be considered for review and potential publication in your esteemed Journal. Our research aims to optimize the scalability challenge currently faced by blockchain systems. Currently, we have a few layer-2 solutions to counter the scalability problem and increase overall throughput, but they too face challenges. We aim to optimize one of the existing layer-2 solutions i.e Optimistic Rollups. However, these layer-2 solutions are easier to build, but transaction settlement time can take up to seven days. This is due to stringent fraud detection mechanisms. As a result, the cost of withdrawing money from the layer-1 blockchain layer is higher. EFD Framework reduces the challenger’s verification time and hence, cuts the overall cost to the user. EFD Framework uses some novel techniques like the Bloom-Merkle filter on Patricia trees and the User As Verifier model to enhance the layer-2 solutions and provide early fraud detection. We have developed a Testbed for the experiment, used smart contract endpoints, and deployed it on Ethereum Mainnet Test Network to test the feasibility of the proposed scheme. The data from the Testbed illustrate the high efficacy of the proposed approach. Our framework provides high efficiency compared with state-of-art schemes and up to 25% reduction of the total cost to users. Our framework withstands various security attacks like Double spending, Sybil attacks, Modification attack, and Denial-of-Service (DOS) attacks. We confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. Please address all correspondence concerning this manuscript to me at debasis@iitj.ac.in Thank you for your consideration of this manuscript. Sincerely, Ms. Shristy Gupta Mr. Amritesh Kumar Mr. Lokendra Vishwakarma Dr. Debasis Das.

Funding

This work is partially supported by SERB (Project No. MTR/2023/001153) and DST IBITF Project (Project Number: IBITF/Note/EIR-PRAYAS/Cohort-03/SanctionLetter/2024-25/0076).

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Author notes

  1. Shristy Gupta, Amritesh Kumar and Lokendra Vishwakarma have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology, Jodhpur, India

    Shristy Gupta, Lokendra Vishwakarma & Debasis Das

  2. IDRP: IoT and Applications, Indian Institute of Technology, Jodhpur, India

    Amritesh Kumar

  3. Indian Institute of Technology Jodhpur, Jodhpur, India

    Shristy Gupta, Amritesh Kumar, Lokendra Vishwakarma & Debasis Das

Authors
  1. Shristy Gupta
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  4. Debasis Das
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Contributions

EFD: A Novel Framework for Early Fraud Detection in Optimistic Rollups Author 1: Shristy Gupta Shristy Gupta is the first author of the manuscript. She conceived and designed the analysis part. She collected the data and implemented it on Amazon Web Services (AWS). She also wrote and reviewed the manuscript. Author 2: Amritesh Kumar Amritesh Kumar is the second author of the manuscript. He contributed equally to the manuscript preparation. He surveyed the state-of-the-art approaches. He contributed equally to technical content and did the security analysis. He also wrote and reviewed the manuscript. Author 3: Lokendra Vishwakarma Lokendra Vishwakarma is the third author of the paper. He contributed equally to the manuscript preparation. He contributed equally to technical content and did the performance analysis. He prepared figures from 1-7. He also wrote and reviewed the manuscript. Author 4: Debasis Das Debasis Das is the fourth author of the paper. He also contributed to the conception and design, analysis, and interpretation of the data. Drafting the article or revising it critically for important intellectual content and approval of the final version.

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Correspondence to Amritesh Kumar.

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Gupta, S., Kumar, A., Vishwakarma, L. et al. Enhancing blockchain scalability and security: the early fraud detection (EFD) framework for optimistic rollups. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04471-x

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