Research Article
Ashutosh kumar singh
Journal
International Journal of Digital Applications and Contemporary Research (IJDACR)
ISSN
2319-4863
Volume / Issue
Vol.14 · Issue 3
Published
October 2025
Access
Open Access
Licence
CC BY-NC-SA 4.0
With the increasing reliance on data-driven decision systems in financial and cyber-physical infrastructures, detecting anomalous and high-risk events has become a critical challenge. Traditional statistical methods and static machine learning models often fail to adapt to evolving data distributions, rare events, and adversarial behaviors. This paper proposes an optimization-driven adaptive anomaly detection framework that integrates deep learning, evolutionary parameter optimization, and robust decision mechanisms. The approach is evaluated through MATLAB-based simulations on multivariate time-series data representing financial transactions and system activity patterns. The proposed architecture combines deep autoencoder-based feature extraction with adaptive threshold optimization, enhancing detection accuracy and robustness in non-stationary environments. Experimental results demonstrate superior performance over baseline models in terms of accuracy, precision, recall, and false alarm rate. The findings highlight the effectiveness of optimization-based adaptive learning for sensitive risk monitoring systems and suggest future directions for large-scale and real-time deployment.
Ashutosh kumar singh (2025). Optimization-Driven Adaptive Anomaly Detection for Financial and Cyber-Physical Systems. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.14, Issue 3. ISSN: 2319-4863.
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