Machine Learning-Driven Anomaly Detection for Real-Time Cyber Threat Mitigation in Digital Financial and Crypto-Asset Ecosystems

Abstract

Conventional cybersecurity approaches have failed to address the vulnerabilities introduced by digital financial systems and crypto-assets marketplaces. This study examines the efficacy of machine learning-based anomaly detection systems in identifying potential cyber threats within the digital financial ecosystem. The methodology involves an extensive analysis of existing problems in cybersecurity, a comparative study of machine learning techniques and traditional cybersecurity methods, and real-world events such as the Bitfinex hack and DeFi exploits. Key findings from the study indicate that methods based on machine learning can achieve up to 35% faster anomaly detection and are 40% more accurate than statistical and rule-based approaches. Meanwhile, precise implementation of the technique has not yet been fully attained due to the high rate of false positives, computational delay, and failure to adjust to changes in the environment. The study emphasizes the importance of hybrid human-AI systems in achieving the best results. This means that for financial institutions to remain competitive and resilient in cybersecurity, they need to implement those systems effectively by ensuring compliance with regulation requirements.