Blockchain relies on the underlying peer-to-peer (P2P) networking to broadcast and get up-to-date on the blocks and transactions. Because of the blockchain operations’ reliance on the information provided by P2P networking, it is imperative to have high P2P connectivity for the quality of the blockchain system operations and performances. High P2P networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless Bitcoin cryptocurrency network, using the peer identifiers – including the current approach of counting the number of distinct IP addresses and port numbers – can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating identities. We build a robust estimation engine for the P2P networking connectivity by sensing and processing the P2P networking traffic. We take a systematic approach to study our engine and analyze the followings: the different components of the connectivity estimation engine and how they affect the accuracy performances, the role and the effectiveness of an outlier detection to enhance the connectivity estimation, and the engine’s interplay with the Bitcoin protocol. We implement a working Bitcoin prototype connected to the Bitcoin mainnet to validate and improve our engine’s performances and evaluate the estimation accuracy and cost efficiency of our connectivity estimation engine. Our results show that our scheme effectively counters the identity-manipulations threats, achieves 96.4% estimation accuracy with a tolerance of one peer connection, and is lightweight in the overheads in the mining rate, thus making it appropriate for the miner deployment.
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Lightweight and Identifier-Oblivious Engine for Cryptocurrency Networking Anomaly Detection
The distributed cryptocurrency networking is critical because the information delivered through it drives the mining consensus protocol and the rest of the operations. However, the cryptocurrency peer-to-peer (P2P) network remains vulnerable, and the existing security approaches are either ineffective or inefficient because of the permissionless requirement and the broadcasting overhead. We design a Lightweight and Identifier-Oblivious eNgine (LION) for the anomaly detection of the cryptocurrency networking. LION is not only effective in permissionless networking but is also lightweight and practical for the computation-intensive miners. We build LION for anomaly detection and use traffic analyses so that it minimally affects the mining rate and is substantially superior in its computational efficiency than the previous approaches based on machine learning. We implement a LION prototype on an active Bitcoin node to show that LION yields less than 1% of mining rate reduction subject to our prototype, in contrast to the state-of-the-art machine-learning approaches costing 12% or more depending on the algorithms subject to our prototype, while having detection accuracy of greater than 97% F1-score against the attack prototypes and real-world anomalies. LION therefore can be deployed on the existing miners without the need to introduce new entities in the cryptocurrency ecosystem.
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- Award ID(s):
- 1922410
- NSF-PAR ID:
- 10324004
- Date Published:
- Journal Name:
- IEEE Transactions on Dependable and Secure Computing
- ISSN:
- 1545-5971
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TDSC.2022.3152937
