CRYPTO CURRENCY: CHALLENGES, SECURITY ISSUES, AND FUTURE DIRECTIONS
Keywords:
Cryptocurrency, Blockchain Technology, Machine Learning, Price Prediction, Bitcoin, Ethereum, Ensemble Learning, Random Forest, CatBoost, Financial Forecasting, Mining Algorithms, Consensus Mechanisms, Market Volatility, Deep Learning, Time-Series Analysis.Abstract
Cryptocurrencies are decentralized digital assets that operate on block chain technology, enabling secure peer-to-peer transactions without reliance on central authorities. Since Bit coin’s introduction in 2009, these systems have increasingly influenced modern financial markets. A key component is mining, which validates transactions, maintains the distributed ledger, and generates new currency units. However, cryptocurrencies are characterized by significant price volatility and face challenges such as security vulnerabilities, regulatory uncertainty, and high energy consumption. This study examines the underlying technology, mining strategies, and potential risks associated with cryptocurrencies. It also investigates the effectiveness of machine learning techniques for price prediction. A dataset covering the period from 2017 to 2022, including price and trading volume data for Bitcoin, Ethereum, Binance Coin, and Tether (USDT), was analyzed. Models such as K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and CatBoost were evaluated using R-squared, Mean Squared Error, and Root Mean Squared Error metrics. Experimental results show that ensemble methods, particularly Random Forest and CatBoost, achieve better predictive performance. The findings demonstrate both the potential and limitations of cryptocurrencies, highlighting the importance of data-driven decision-making in this volatile domain.
