Abstract
Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.
Recommended Citation
C. Rawlins and J. Sarangapani, "An Intelligent Distributed Ledger Construction Algorithm for IoT," IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Jan 2022.
The definitive version is available at https://doi.org/10.1109/ACCESS.2022.3146343
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Blockchain Security; Blockchains; Distributed ledger; Distributed Ledger Technology; Internet of Things; Internet of Things; Machine Learning; Machine learning; Multi-arm Bandit; Performance evaluation; Protocols; Regret Analysis; Security
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
26 Jan 2022
Comments
This work was supported in part by the Graduate Assistance in Areas of National Need (GAANN) National Fellowship Program; and in part by the Department of Energy's Kansas City National Security Campus, operated by Honeywell Federal Manufacturing and Technologies, LLC, under Contract DE-NA0002839.