Bitcoin ore pool attack strategy learning algorithm based on random game reinforcement learning

A technology of enhanced learning and policy learning, applied in machine learning, computing, computing models, etc.

Active Publication Date: 2019-11-15
WUHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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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  • Bitcoin ore pool attack strategy learning algorithm based on random game reinforcement learning
  • Bitcoin ore pool attack strategy learning algorithm based on random game reinforcement learning
  • Bitcoin ore pool attack strategy learning algorithm based on random game reinforcement learning

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Experimental program
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Embodiment specific Embodiment approach

[0079] The process of finding an approximate Nash equilibrium to obtain an approximate optimal strategy pair for this iteration, as shown in the figure, is the payoff matrix when two mining pools take different actions. Assuming ∈ = 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.

[0080] 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.

[0081] The concrete implementation process of embodiment is as follows:

[0082] The state value is the benefit in different states. Suppose the initial state of mining pool 1 is denoted as s 0 , the...

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Abstract

The invention discloses a Bitcoin ore pool attack strategy learning algorithm based on random game reinforcement learning. A method for maximizing the future expected income is adopted, so that the ore pool adaptively and dynamically adjusts the attack behavior in the learning process, and the approximate optimal attack strategy in different bitcoin network environments is obtained according to the learning result, so that the opponent ore pool is weakened to the greatest extent, and the income of the ore pool is maximized. The influence of distributed denial of service attacks on the dynamicdevelopment of a bitcoin ore pool is analyzed; the problem that the bitcoin ore pools adaptively select the optimal attack strategy capable of maximizing the long-term income in a dynamic environmentis solved, the competitive attack between the bitcoin ore pools is modeled as a normal and random game, and the optimal attack strategy is obtained by using 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 Applications(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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