In this study, we present an advanced epidemic model designed to analyze the spread of active infectious nodes within computer networks under the influence of malicious code. Our model employs the basic reproduction number to gauge the potential for transmission of the malicious code throughout the network. Through rigorous analysis, we identify equilibrium points and assess their stability, which is crucial for predicting the epidemic's long-term behavior. Specifically, we find that when , the number of infected nodes will decrease naturally, leading to the containment and eventual eradication of the malicious code. Conversely, when , the infection persists, perpetuating the spread of malicious code across the network. We leverage MATLAB to solve and simulate the governing differential equations, providing insights into the epidemic's temporal and spatial spread. To deepen our understanding, we apply linear regression analysis to the simulation data. This approach allows us to elucidate the relationships between key parameters and the dynamics of the epidemic, quantifying their effects on transmission potential and system stability. By integrating these insights, we enhance our ability to predict and mitigate the impact of future malicious code outbreaks, thereby strengthening network security and resilience.