ML techniques are eliminating critical issues in cryptocurrency and blockchain.

Cryptocurrency and blockchain are transforming our world, especially financially. Bitcoin and Ethereum have reached their all-time high prices in 2021 and with new version launches, the market is only going to become more exciting. New applications of blockchain technology, especially in NFTs are also an added factor. But like any other technology, blockchain and cryptocurrencies have some security concerns. To solve these critical blockchain-related problems, machine learning is being used in efficient ways. 

1. Reinforcement Learning for Trading 

Trading cryptocurrencies like Bitcoin and Ethereum have become popular among retail investors and large financial institutions. Trading bots used in the stock market are powered by machine learning algorithms. Hence, ML techniques can also be used in the crypto market for trading. Reinforcement learning can develop cryptocurrency trading strategies for a profitable and adaptive experience. 

2. Optimizing Mining Strategies 

Mining cryptocurrency is related to using computing resources to solve a blockchain function. Miners are rewarded for their efforts with block rewards. So, the more powerful the computer is, the easier it is to solve blockchains functions. To optimize mining efforts and prevent wrongful usage of mining resources, ML techniques can be used. Traditional reinforcement learning algorithms can maximize the rewards with better mining strategies. 

3. Using Deep Learning to Tackle Cryptojacking 

Security is a prevalent issue in cryptocurrency mining. Academic institutions and government agencies with big computing infrastructures are prime targets for cryptojacking. Deep learning can help detect the presence of malicious programs that are intended to hijack computing resources. Machine learning applications in the world of blockchain and cryptocurrencies go beyond forecasting the prices. ML holds the capacity to answer the security concerns in this technology with deep learning and reinforcement learning by optimizing the behind-the-scene functions of crypto trading and mining.