Analysis of Blockchain Selfish Mining Attacks

Kędziora, Michał; Kozłowski, Patryk; Szczepanik, Michał; Jóźwiak, Piotr

doi:10.1007/978-3-030-30440-9_22

Cited by 23 publications

(12 citation statements)

References 6 publications

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“…Several authors have suggested ways to detect this attack but stopped short of doing any research into it (Göbel et al, 2016;Eyal and Sirer, 2018). A few papers researched detection but only focused on a single factor or only worked on detection in a single stage (Chicarino et al, 2020;Kędziora et al, 2020;Liu et al, 2020). Blockchain developers have released patches to prevent selfish mining, but without reliable detection, it is impossible to tell if these patches were successful.…”

Section: Matthew Peterson Todd Andel and Ryan Bentonmentioning

confidence: 99%

“…Another paper examined different combinations of parameters and how they influenced the vulnerability of the blockchain network (Kędziora et al, 2020). The parameters under study were the difference between the selfish miner's chain and the honest chain, the number of connections made by the selfish miners, and the total hashing power of the selfish miners.…”

Section: Matthew Peterson Todd Andel and Ryan Bentonmentioning

confidence: 99%

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Towards Detection of Selfish Mining Using Machine Learning

Peterson

Andel

Benton

2022

iccws

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Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. The selfish miners continue to mine on their private chain while the honest miners waste resources mining on a shorter chain. According to the blockchain protocol, a longer chain takes precedent and shorter chains are discarded which allows the selfish miners to gain an advantage by keeping their chain secret. This attack can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from cryptocurrency exchanges. Several of these attacks were launched in 2018 and 2019 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Although this attack is possible against both Proof-of-Work and Proof-of-Stake blockchains, this research concentrates on detection in Proof-of-Work blockchains. As is difficult to evaluate security advances in the real-time blockchain, it is imperative to focus on simulation to evaluate blockchain security properties. To this end, we extend a blockchain simulator and add the ability to simulate selfish mining attacks. Several existing simulators are examined before choosing SimBlock for this research. Our goal is to identify the factors that identify selfish mining. Using existing research, we choose several factors that could identify an attack in an unlaunched state, an active state, or historically. We plan to use simulated data to train a machine learning model to detect selfish mining. Using the modified simulator, we generate training and test data for unlaunched and active attacks. For historical attacks, we will use historical data from known selfish mining attacks. While some existing research has examined the detection of selfish mining, it only examines active attacks. In this paper, we seek to lay the groundwork for future research into detecting attacks that are unlaunched, active, or historical.

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Section: Matthew Peterson Todd Andel and Ryan Bentonmentioning

confidence: 99%

Section: Matthew Peterson Todd Andel and Ryan Bentonmentioning

confidence: 99%

Towards Detection of Selfish Mining Using Machine Learning

Peterson

Andel

Benton

2022

iccws

View full text Add to dashboard Cite

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“…Due to the dynamic fluctuations of the network conditions of the miners in reality, the first block of a fork that is propagated to a miner in irrelevant mining pools can be arbitrary and hence the chain selection of a miner in irrelevant mining pools is modeled as a random choice. Note that similar to existing studies [11], [12], in this modeling study we do not consider the selfish mining attacks by some minority miners [19] to enable tractable analysis.…”

Section: Temporary Fork Modelingmentioning

confidence: 99%

Impact of Temporary Fork on the Evolution of Mining Pools in Blockchain Networks: An Evolutionary Game Analysis

Chen

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Temporary fork is a fundamental phenomenon in many blockchains with proof of work, and the analysis of temporary fork has recently drawn great attention. Different from existing efforts that focus on the blockchain system factors such as block size, network propagation delay or block generation speed, in this paper we explore a new key dimension of computing power from the miners' perspective. Specifically, we first propose a detailed mathematical model to characterize the impact of computing power competition of the mining pools on the temporary fork. We also derive closed-form formula of the probability of temporary fork and the expected mining reward of a mining pool. To reveal the long-term trends on the computing power distributions over the competing mining pools, we then develop an evolutionary game framework based on the temporary fork modeling and accordingly characterize the set of stable evolution equilibriums. Both extensive numerical simulations and realistic blockchain data based evaluation provide evidence to support our theoretical models and discoveries.

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“…Data and experiment analysis. Other research work needs to be mentioned, for example, Wright [93], Wright and Savanah [94], Eijkel and Fehnker [21] and Kedziora et al [44].…”

Section: Advances In Blockchain Selfish Miningmentioning

confidence: 99%

A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining

Li¹,

Chang²,

Wu³

et al. 2020

Preprint

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In this paper, we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorously mathematical setting. To this end, we first describe a more general blockchain selfish mining with both a two-block leading competitive criterion and a new economic incentive, and establish a pyramid Markov process to express the dynamic behavior of the selfish mining from both consensus protocol and economic incentive. Then we show that the pyramid Markov process is stable and so is the blockchain, and its stationary probability vector is matrix-geometric with an explicitly representable rate matrix. Furthermore, we use the stationary probability vector to be able to analyze the waste of computational resource due to generating a lot of orphan (or stale) blocks. Nextly, we set up a pyramid Markov reward process to investigate the long-run average profits of the honest and dishonest mining pools, respectively. Specifically, we show that the long-run average profits are multivariate linear such that we can measure the improvement of mining efficiency of the dishonest mining pool comparing to the honest mining pool. As a by-product, we build three approximative Markov processes, related to the pyramid Markov process, when the system states are described as the block-number difference of two forked block branches. Also, by using their special cases with non network latency, we can further provide some useful interpretation for both the Markov chain (Figure 1) and the revenue analysis ((1) to(3)) of the seminal work by Eyal and Sirer (2014). Finally, we use some numerical examples to verify the correctness and computability of our theoretical results. Note that the pyramid Markov (reward) processes can be applied to open a new avenue in the study of blockchain selfish mining from a perspective of normative computing, thus we hope that the methodology and results developed in this paper shed light on the blockchain selfish mining such that a series of promising research can be produced potentially.

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Analysis of Blockchain Selfish Mining Attacks

Cited by 23 publications

References 6 publications

Towards Detection of Selfish Mining Using Machine Learning

Towards Detection of Selfish Mining Using Machine Learning

Impact of Temporary Fork on the Evolution of Mining Pools in Blockchain Networks: An Evolutionary Game Analysis

A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining

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