Block Mining reward prediction with Polynomial Regression, Long short-term memory, and Prophet API for Ethereum blockchain miners

Abstract

The Ethereum blockchain is an open-source, decentralized blockchain with functions triggered by smart contract and has voluminous real-time data for analysis using machine learning and deep learning algorithms. Ether is the cryptocurrency of the Ethereum blockchain. Ethereum virtual machine is used to run Turing complete scripts. The data set concerning a block in the Ethereum blockchain with a block number, timestamp, crypto address of the miner, and the block rewards for the miner are explored for K means clustering for clustering miners with a unique crypto address and their rewards. Linear regression and polynomial regression are used for the prediction of the next block reward to the miner. The Long ShortTerm Memory (LSTM) algorithm is used to exploit the Ether market data set for predicting the next ether price in the market. Every kind of price and volume for every four hours is taken for prediction. The root mean square error of 34.9% is obtained for linear regression, the silhouette score is 71% for K-means clustering of miners with same rewards, with the optimal number of clusters obtained by Gap statistic method.

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum

“…Simon et al anticipated mining rewards using polynomial regression for Ethereum. However, they did not consider time in predicting future values, possibly because the studied blockchains were public [ 5 ].…”

“…For instance, Ji et al measured the number of transactions to detect attacks on the Bitcoin network [ 4 ]. Similarly, Simon et al proposed an intrusion detection system for the Ethereum network by analyzing mining rewards [ 5 ]. Additionally, various other previous works focus on detecting DDoS attacks on blockchain systems.…”

Autopsy of Ethereum's Post-Merge Reward System

“…Pools where individual miners unified efforts to split rewards and costs, making them more stable over time. Other works like [12] presented an approach that, using linear and polynomial regressions, could predict the mining reward of the next Ethereum block to some degree. More focused on protocol optimizations, other works like [13] introduced an intensive study highlighting the under-optimized storage patterns that most smart contracts use.…”