Boosted training strategy for physics-informed neural networks in modeling non-linear computer virus dynamics with vertical transmission

Computer-virus propagation in networked systems is often modeled by epidemic-type derivative equations. Still, standard quantitative solvers can become computationally demanding and may exhibit small stability for long-term predictions. This work develops a Physics-Informed Neural Network (PINN) based framework for an e-epidemic SI 1 I 2 R model of computer-virus spread that incorporates a possibly transmissible class, an amply transmissible class, and direct transmission, allowing nodes to be initially compromised without contact. The proposed PINN uses a multilayer, amply connected architecture with Swish-ReLU activations to approximate the solution to the coupled nonlinear common differential equations and to enforce the governing dynamics through a physics-based loss. We use standard error metrics such as Mean Squared Error (MSE), Theil’s Inequality Coefficient (TIC), and Mean Absolute Deviation (MAD) to compare the model's performance against a reference ODE Solver. We also examine training and validation loss profiles across several trials. A grid-based sensitivity analysis of key parameters (the infection rate and the progression rate from I 1 to I 2 ) is conducted to determine how they affect the peak sizes of infected and recovered populations. The results show that the PINN accurately reproduces the reference SI 1 I 2 R dynamics with low errors across all compartments. Swish-ReLU is the most stable and accurate training function of the ones that were tested. These results indicate that PINNs are a reliable and possibly resource-efficient way to model and predict how computer viruses spread. They also show that they could be useful for future AI-based epidemic modeling in cybersecurity.