Selfish Mining Re-Examined

Negy, Kevin; Rizun, Peter R.; Sirer, Emin Gün

doi:10.1007/978-3-030-51280-4_5

Cited by 40 publications

(24 citation statements)

References 17 publications

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“…A selfish mining attack relies on "block concealing" and make the network adapt to their block solutions and claim block rewards [25,69]. In selfish mining, the selfish miner or selfish mining pool with a hash rate of α secretly withholds newly mined blocks instead of publishing or distributing them to the rest of the network [70]. This private chain will continue to mine until they have reached a greater length than the main chain.…”

Section: F Selfish Mining Attackmentioning

confidence: 99%

Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach

et al. 2021

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Section: F Selfish Mining Attackmentioning

confidence: 99%

Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach

et al. 2021

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“…Further, a valid block is immediately announced, once found, into the FoBSim network, while in some applications, there might be a conditional delay. For instance, if a selfish mining attack scenario is to be simulated, miners would prefer to keep their newly found blocks secret, hoping they will find the next block as well ( Negy, Rizun & Sirer, 2020 ).…”

Section: The Fobsim Environmentmentioning

confidence: 99%

FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems

Baniata¹,

Kertész²

2021

PeerJ Computer Science

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A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim’s novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate case studies, then present and analyze the obtained results, where deploying the BC network in the fog layer shows enhanced efficiency in terms of total run time and total storage cost.

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“…proposed the concept of selfish mining and pointed out its harmfulness; a series of studies on this attack have appeared [4,10,17,18,19,20].…”

Section: Motivation and Contribution Since Eyal And Sirermentioning

confidence: 99%

ForkDec: Accurate Detection for Selfish Mining Attacks

Wang

et al. 2021

Security and Communication Networks

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Incentive mechanism is the key to the success of the Bitcoin system as a permissionless blockchain. It encourages participants to contribute their computing resources to ensure the correctness and consistency of user transaction records. Selfish mining attacks, however, prove that Bitcoin’s incentive mechanism is not incentive-compatible, which is contrary to traditional views. Selfish mining attacks may cause the loss of mining power, especially those of honest participants, which brings great security challenges to the Bitcoin system. Although there are a series of studies against selfish mining behaviors, these works have certain limitations: either the existing protocol needs to be modified or the detection effect for attacks is not satisfactory. We propose the ForkDec, a high-accuracy system for selfish mining detection based on the fully connected neural network, for the purpose of effectively deterring selfish attackers. The neural network contains a total of 100 neurons (10 hidden layers and 10 neurons per layer), learned on a training set containing about 200,000 fork samples. The data set, used to train the model, is generated by a Bitcoin mining simulator that we preconstructed. We also applied ForkDec to the test set to evaluate the attack detection and achieved a detection accuracy of 99.03%. The evaluation experiment demonstrates that ForkDec has certain application value and excellent research prospects.

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Selfish Mining Re-Examined

Cited by 40 publications

References 17 publications

Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach

Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach

FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems

ForkDec: Accurate Detection for Selfish Mining Attacks

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