The threat of malware is increasing and poses significant computer security risks to both individuals and organizations. Understanding the tactics employed by these malicious software entities and their dynamics during the epidemic process is crucial for designing robust defense strategies and ensuring the protection of computer systems. In this article, we investigate a population of digital nodes (such as phones, computers, \(\dots \) ) under attack by modeling the network using a susceptible-infected-resistant (SI2R) compartmental model, where hosts can transition between susceptible, infected, or resistant states. Our model considers two types of infected nodes: active nodes, whose resources are exploited, and passive nodes, which spread the virus. Both active and passive nodes can develop resistance with certain probabilities, which are influenced by the resource utilization percentages set by the malware designer. Thus, rather than optimizing resource utilization, the malware’s goal is to maximize the number of active hosts at the end of the process. To achieve this objective, we aim to determine the optimal percentage of passive nodes to consider at each period, recognizing that the variation in the number of active nodes depends on the number of passive nodes.