Toward a Machine Learning and Software Defined Network Approaches to Manage Miners’ Reputation in Blockchain

Abstract

In blockchain, transactions between parties are regrouped into blocks, in order to be added to the blockchain’s distributed ledger. Miners are nodes of the network that generate new blocks according to the consensus protocol. The miner that adds a valid block to the distributed ledger is rewarded. However, to find a valid block, the miner needs to solve a computationally difficult problem, which makes it difficult to a single miner to gain rewards. Therefore, miners join mining pools, where the powers’ of miners are federated to ensure stable revenues. In public blockchains, access to mining pools is not restricted, which makes mining pools vulnerable to considerable threats such as: block withholding (BWH) attacks and distributed denial of service (DDoS) attacks. In the present work, we propose a new reputation based blockchain named PoolCoin based on a distributed trust model for a mining pools. The trust model used by PoolCoin is inspired from the job market signaling model. The proposed PoolChain blockchain allows pool managers the selection of trusted miners in their mining pools, while miners are able to evaluate them. Furthermore, to detect malicious miners that claim bigger computing capacity, we also provided a machine learning module to estimate the real miners’ capacities. The efficiency of the proposed trust model is studied and the obtained simulation results are presented and discussed. Thus, the model parameters’ are optimized in order to detect and exclude misbehaving miners, while honest miners are maintained in the mining pool.