extended-abstract

WeRLman: to tackle whale (transactions), go deep (RL)

Authors:

Roi Bar-Zur,

Ameer Abu-Hanna,

Ittay Eyal,

Aviv TamarAuthors Info & Claims

SYSTOR '22: Proceedings of the 15th ACM International Conference on Systems and Storage

Page 148

https://doi.org/10.1145/3534056.3535005

Published: 06 June 2022 Publication History

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Abstract

Blockchain technology is responsible for the emergence of cryptocurrencies, such as Bitcoin and Ethereum. The security of a blockchain protocol relies on the incentives of its participants. Selfish mining is a form of deviation from the protocol where a participant can gain more than her fair share. Previous analyses of selfish mining make easing, non-realistic assumptions. We introduce a more realistic model with varying block rewards in the form of transaction fees. However, this comes at the cost of an intractable state space. To solve the complex model, we introduce WeRLman, a novel method based on deep Reinforcement Learning (deep RL). Using WeRLman, we show reward variability can significantly hurt blockchain security.

References

[1]

Roi Bar-Zur, Ameer Abu-Hanna, Ittay Eyal, and Aviv Tamar. 2022. WeRLman: To Tackle Whale (Transactions), Go Deep (RL). Cryptology ePrint Archive (2022).

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[2]

Ayelet Sapirshtein, Yonatan Sompolinsky, and Aviv Zohar. 2016. Optimal selfish mining strategies in bitcoin. In International Conference on Financial Cryptography and Data Security. Springer, 515--532.

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Cited By

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Bar-Zur RDori DVardi SEyal ITamar A(2023)Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL2023 IEEE Security and Privacy Workshops (SPW)10.1109/SPW59333.2023.00008(29-37)Online publication date: May-2023
https://doi.org/10.1109/SPW59333.2023.00008
Papadis NTassiulas L(2023)Deep Reinforcement Learning-Based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel NetworksMathematical Research for Blockchain Economy10.1007/978-3-031-48731-6_1(1-27)Online publication date: 15-Dec-2023
https://doi.org/10.1007/978-3-031-48731-6_1

Index Terms

WeRLman: to tackle whale (transactions), go deep (RL)

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Information & Contributors

Information

Published In

SYSTOR '22: Proceedings of the 15th ACM International Conference on Systems and Storage

June 2022

163 pages

ISBN:9781450393805

DOI:10.1145/3534056

General Chairs:
Michal Malka
IBM Research - Haifa, Israel
,
Hillel Kolodner
IBM Research - Haifa, Israel
,
Program Chairs:
Frank Bellosa
Karlsruhe Institute of Technology, Germany
,
Moshe (Mickey) Gabel
University of Toronto, Canada

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

Technion: Israel Institute of Technology
USENIX Assoc: USENIX Assoc

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2022

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Author Tags

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Extended-abstract

Funding Sources

IC3
Israel National Cyber Directorate
Israel Science Foundation
Technion Hiroshi Fujiwara Cyber Security Research Center

Conference

SYSTOR '22

Sponsor:

SIGOPS

SYSTOR '22: The 15th ACM International Systems and Storage Conference

June 13 - 15, 2022

Haifa, Israel

Acceptance Rates

SYSTOR '22 Paper Acceptance Rate 12 of 41 submissions, 29%;

Overall Acceptance Rate 108 of 323 submissions, 33%

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Cited By

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Bar-Zur RDori DVardi SEyal ITamar A(2023)Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL2023 IEEE Security and Privacy Workshops (SPW)10.1109/SPW59333.2023.00008(29-37)Online publication date: May-2023
https://doi.org/10.1109/SPW59333.2023.00008
Papadis NTassiulas L(2023)Deep Reinforcement Learning-Based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel NetworksMathematical Research for Blockchain Economy10.1007/978-3-031-48731-6_1(1-27)Online publication date: 15-Dec-2023
https://doi.org/10.1007/978-3-031-48731-6_1

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