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Neuro-evolutionary computing approach for an epidemic model of ransomware detection using morlet wavelet neural
Zain Ul Abideen Khan1, Muhammad Abid Mughal1, Irfan Ul Haq1
1Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, 45650, Pakistan.
Scientific Reports
|May 16, 2026
Summary
This study introduces a novel neural network approach using Morlet wavelets to model malware dynamics. The method demonstrates high accuracy and predictive power, offering an effective alternative to traditional numerical solvers for malware systems.
Area of Science:
- Computational mathematics
- Cybersecurity
- Applied mathematics
Background:
- Malware behavior can be modeled using nonlinear differential equations.
- Traditional numerical solvers may face challenges in accuracy and convergence for complex malware dynamics.
Purpose of the Study:
- To present a new algorithmic paradigm for modeling malware dynamics.
- To utilize neural networks with Morlet wavelets for state variable estimation.
- To enhance network performance through heuristic optimization.
Main Methods:
- A nonlinear malware model represented by differential equations.
- Morlet wavelet neural networks for state variable estimation.
- A heuristic optimization algorithm for parameter tuning.
- A fitness criterion based on equation residuals and initial conditions.
Main Results:
- Achieved Mean Squared Errors (MSE) between [Formula: see text] and [Formula: see text], indicating high accuracy.
- Theil Inequality Coefficients (TIC) ranged from [Formula: see text] to [Formula: see text], demonstrating strong predictive capabilities.
- Optimization process stability confirmed through comparative runs.
Conclusions:
- The proposed framework effectively models malware dynamics.
- The approach offers a viable and accurate alternative to conventional numerical solvers.
- The results show excellent agreement with reference solutions.
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