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A Study Method of Bitcoin Mining Pool Attack Strategy Based on Stochastic Game Reinforcement Learning

A technology of enhanced learning and policy learning, applied in machine learning, digital transmission systems, instruments, etc.

Active Publication Date: 2020-11-03
WUHAN UNIV
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Problems solved by technology

However, the existing research is only carried out under the static model, and the game equilibrium and the optimal strategy of the mining pool are also static

Method used

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  • A Study Method of Bitcoin Mining Pool Attack Strategy Based on Stochastic Game Reinforcement Learning
  • A Study Method of Bitcoin Mining Pool Attack Strategy Based on Stochastic Game Reinforcement Learning
  • A Study Method of Bitcoin Mining Pool Attack Strategy Based on Stochastic Game Reinforcement Learning

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Embodiment specific Embodiment approach

[0078] Figure 4 shows a simplified example of the process of finding an approximate Nash equilibrium to obtain an approximate optimal strategy pair for this iteration. The figure shows the payoff matrix when the two mining pools take different actions. Assuming ò = 0.01, then the approximate optimal action in state s eventually converges to last pair best policy pair in current state The action selection strategy is updated.

[0079] Step 6, according to Update the state value V of mining pool 1 in state s 1 t+1 (s); according to Update the state value of mining pool 2 in state s Where δ∈[0,1) is the learning rate of the mining pool, and in the tth iteration, the size is δ decreases as the number of iterations increases, which is helpful for the convergence of the algorithm in the later stage.

[0080] The concrete implementation process of embodiment is as follows:

[0081] The state value is the benefit in different states. Suppose the initial state of mini...

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Abstract

The invention discloses a method for learning the attack strategy of a bitcoin mining pool based on random game enhanced learning, adopting a method of maximizing future expected income, allowing the mining pool to adaptively and dynamically adjust the attack behavior during the learning process, and obtaining different strategies according to the learning results. An approximate optimal attack strategy in the Bitcoin network environment to weaken the opponent's mining pool to the greatest extent and maximize the profit of the mining pool. By analyzing the impact of distributed denial-of-service attacks on the dynamic development of bitcoin mining pools, the problem of bitcoin mining pools adaptively selecting the best attack strategy that can maximize long-term benefits in a dynamic environment is solved, and the competition between bitcoin mining pools The attack is modeled as a constant sum random game, and the optimal attack strategy is obtained by a reinforcement learning algorithm.

Description

technical field [0001] The invention belongs to the field of bitcoin mining pool game and reinforcement learning, and in particular relates to a reinforcement learning method of bitcoin mining pool attack random game strategy. Background technique [0002] As the first fully decentralized cryptocurrency, Bitcoin has attracted much attention since it appeared in the public eye. Its security is secured by a group of nodes that keep track of their work on a data structure known as a blockchain. All current and historical transaction records in the Bitcoin system are recorded on the blockchain. In order to motivate these nodes to keep accounts honestly and correctly, and to maintain the integrity of the books, Bitcoin adopts a proof-of-work mechanism. This mechanism requires nodes to prove their computing power by solving cryptographic puzzles. Only nodes that have obtained the correct solution can be qualified for bookkeeping, and can get a certain amount of bitcoin rewards....

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06G06N20/00
CPCH04L63/1416H04L63/1458H04L2209/56H04L9/50
Inventor 王骞陈艳姣吴双可胡胜山
Owner WUHAN UNIV
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