AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0605015Keywords:
Artificial Intelligence, Fraud Detection, Cybersecurity, Cloud Computing, Machine Learning, Deep Learning, Anomaly Detection, Data Security, Threat Intelligence, Enterprise Systems, Predictive Analytics, Intrusion DetectionAbstract
The rapid evolution of digital technologies and the widespread adoption of cloud-based enterprise systems have significantly increased the risk of cyber fraud and security breaches. Traditional fraud detection mechanisms often fail to address sophisticated, real-time threats due to their static and rule-based nature. This research proposes an AI-driven secure intelligent framework designed to enhance fraud detection capabilities within cybersecurity and cloud environments. The framework integrates machine learning, deep learning, and anomaly detection techniques to identify suspicious activities in real time. It leverages big data analytics and behavioral analysis to improve detection accuracy while minimizing false positives. Additionally, the model incorporates secure data encryption, identity management, and adaptive authentication to strengthen overall system security. The proposed system is scalable, making it suitable for enterprise-level cloud infrastructures, and is capable of continuous learning to adapt to emerging threats. Experimental analysis demonstrates improved detection rates, reduced response time, and enhanced resilience against evolving cyberattacks. This study contributes to the advancement of intelligent cybersecurity solutions by combining artificial intelligence with robust cloud security strategies, providing a proactive approach to fraud prevention in modern enterprise ecosystems.
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