Abstract
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.
Highlights
Bitcoin is essentially a decentralized, distributed public ledger, which allows anyone or institution to participate in publishing transactions in a client-side manner [1]. e transaction will be collected by the participants in the network and added to the public ledger through a consensus protocol. e consensus protocol adopted by Bitcoin is called Proof-of-Work
To improve the detection accuracy, in this work, we propose a selfish mining attack detection system based on a machine learning classification model, called ForkDec. e system can detect selfish mining attacks in the Bitcoin network with an accuracy rate of 99.03%
We embed the trained model into the ForkDec system and test it on a test set containing 76,686 samples. e test results show that the ForkDec system can achieve a detection accuracy of 99.03% when the fully connected neural network is used as the classification model and 98.76% when using logistic regression
Summary
Bitcoin is essentially a decentralized, distributed public ledger, which allows anyone or institution to participate in publishing transactions in a client-side manner [1]. e transaction will be collected by the participants (called miners) in the network and added to the public ledger through a consensus protocol. e consensus protocol adopted by Bitcoin is called Proof-of-Work. E selfish mining detector [16] proposed by Chicarino et al realized the detection of selfish mining without modifying the Bitcoin protocol It only considers the factor of fork height and does not take other factors into consideration, which leads to a certain misjudgment rate. To improve the detection accuracy, in this work, we propose a selfish mining attack detection system based on a machine learning classification model, called ForkDec. e system can detect selfish mining attacks in the Bitcoin network with an accuracy rate of 99.03%. To accurately detect selfish mining, we trained a classification model based on logistic regression and a fully connected neural network (with 10 hidden layers and 10 neurons per layer) on the training set, respectively, and applied the learned model to ForkDec for attack detection.
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