Abstract

Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security setting, Vector GAN (VecGAN) is proposed to augment blockchain data in classifier training, which combines error-driven learning with Bayesian estimation to alleviate calculations. This two-step approach with augmentation and classification on new transactions is proposed as a novel approach to blockchain decision-making. Experimental prediction accuracy using VecGAN improved up to 3% on simplistic classifiers compared to other state-of-the-art augmentation techniques. Resource consumption in a realistic blockchain setting was reduced while improving block throughput by 50% compared to PoW. Future work will explore Sybil-spam defensive measures for realistic protocol implementation with this approach.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Blockchain; data augmentation; generative adversarial network; Internet of Things (IoT)

International Standard Serial Number (ISSN)

1558-0660; 1536-1233

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jul 2024

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