AI-Driven Cybersecurity Software for Fraud and Network Intrusion Detection in Financial Big Data Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0806019Keywords:
AI, Machine Learning, Cybersecurity, Big Data, Network Intrusion Detection, Fraud Detection, Financial Systems, Banking Security, Trading Systems, Anomaly Detection, Predictive Analytics, Real-Time MonitoringAbstract
The rapid growth of digital banking and financial trading systems has significantly increased the risk of cyber threats, including network intrusions and fraudulent activities. Traditional security mechanisms often struggle to handle the high volume, velocity, and variety of financial big data, leading to delayed threat detection and financial losses. This research proposes an AI-driven cybersecurity software framework that leverages machine learning algorithms, big data analytics, and real-time monitoring to detect and prevent fraudulent transactions and network intrusions. The proposed system integrates anomaly detection, predictive analytics, and automated threat response to enhance security, reduce false positives, and improve operational efficiency. By combining scalable big data processing with intelligent machine learning models, the framework ensures proactive and adaptive cybersecurity for banking and financial trading platforms. Experimental results on real-world financial datasets demonstrate high accuracy, robustness, and efficiency, highlighting the potential of AI-powered solutions in safeguarding critical financial infrastructures.References
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