Adaptive Behavioral Analytics for Intrusion Prevention in Ai-Driven Digital Currency and Financial Cyber Defense Systems

Behavioral analytics is a cutting-edge tool in the fight for financial cybersecurity. It uses advanced AI and machine learning to pinpoint dangers that outdated methods might miss. This study examines how well these AI-based tools work and the challenges encountered, especially when trying to mitigate and prevent security breaches in the digital currency world and financial markets. The case study analysis of three large-scale security incidents, namely a cryptocurrency exchange, a banking institution an advanced persistent threat (APT), and a DeFi platform, identified the current state of behavioral analytics implementation. Key findings show that while AI-based solutions can efficiently identify threats that rely on the volume and behavioral patterns of the underlying systems, they struggle with more refined attacks that exploit legitimate features. Consequently, these systems exhibit high false positives and low response times. The cross-case analysis indicates that the behavioral correlations across domains and the threshold off-peak periods are not adequately addressed. The study offers recommendations on better implementation for algorithm development and data integration as well as policy formulation. Therefore, the main contributions are: 1: Common behavioral indicators can be derived from the financial platform. 2: Human-AI cooperation is required to obtain an effective identification process, and 3: The security and operation continuity requirements can be balanced by adjusting the threshold level in real time.