Machine Learning Driven Analytics for National Security Operations: A  Wavelet–Stochastic Signal Detection Framework

Sujoy Saha ,Md Kamrul Islam,Md Arifur Rahaman,Rabi Sankar Mondal ,Md. Kamruzzaman

Machine Learning Driven Analytics for National Security Operations: A Wavelet–Stochastic Signal Detection Framework

Authors

Sujoy Saha ,Md Kamrul Islam,Md Arifur Rahaman,Rabi Sankar Mondal ,Md. Kamruzzaman

Keywords:

Wavelet Transform; Stochastic Signal Modelling; Anomaly Detection; Discrete Time Series; Machine Learning; Computational Signal Analysis

Abstract

Detecting signals in noisy, dynamic systems is crucial in national security, surveillancesystems, radar detection, and security communications networks. This study aims to achieve amathematically sound signal anomaly detection framework, which incorporates DiscreteWavelet Transform (DWT) with stochastic noise modelling to facilitate robust feature

References

Z. Yu, H. Gao, X. Cong, N. Wu, and H. H. Song, "A survey on cyber–physical systems security," IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21670-21686, 2023, doi: https://doi.org/10.1109/JIOT.2023.3289625.

J. Martínez Torres, C. Iglesias Comesaña, and P. J. García-Nieto, "Machine learning techniques applied to cybersecurity," International Journal of Machine Learning and Cybernetics, vol. 10, no. 10, pp. 2823-2836, 2019, doi: https://doi.org/10.1007/s13042018-00906-1.

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Published

2024-09-20

How to Cite

Sujoy Saha ,Md Kamrul Islam,Md Arifur Rahaman,Rabi Sankar Mondal ,Md. Kamruzzaman. (2024). Machine Learning Driven Analytics for National Security Operations: A Wavelet–Stochastic Signal Detection Framework . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5723–5744. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3349