Preserving Security of Crypto Transactions with Machine Learning Methodologies

The rapid adoption of cryptocurrencies has transformed global financial systems by enabling decentralized and peer-to-peer transactions. Despite strong cryptographic foundations, blockchain-based transactions remain vulnerable to fraud, money laundering, phishing, ransomware payments, and illicit fund transfers. Traditional rule-based and heuristic security mechanisms are insufficient to detect complex and evolving attack patterns in real time. This paper presents a machine-learning-based framework for preserving the security of cryptocurrency transactions. The proposed system analyzes blockchain transaction data using supervised learning techniques and behavioral feature extraction to classify transactions as legitimate or illicit. Features such as transaction volume, frequency, temporal patterns, and wallet behavior are extracted and processed. Multiple machine learning algorithms including Naïve Bayes, Support Vector Machine, Logistic Regression, and Decision Tree are evaluated. Experimental results demonstrate improved detection accuracy, reduced false positives, and enhanced adaptability compared to traditional methods. The proposed framework offers a scalable and intelligent solution for strengthening security in decentralized financial ecosystems.