Abstract: Ransomware has emerged as one of the leading cybersecurity threats to microenterprises, which often lack the technological and financial resources to implement advanced protection systems. This study proposes a cybersecurity model based on machine learning, designed not only for the detection and mitigation of ransomware attacks but also as a scalable and adaptable solution that can be integrated into business infrastructures across various sectors. By leveraging advanced techniques to identify malicious behavior patterns, the system alerts businesses before significant damage occurs. Moreover, this approach provides complementary measures such as automated updates and backups, enhancing resilience against cyber threats in resource-constrained environments. This research aims not only to protect critical data but also to contribute to the development of accessible cybersecurity models, improving operational continuity and promoting sustainability in the digital landscape.

Luis Jesús Romero Castro, Piero Alexander Cruz Aquino and Fidel Eugenio Garcia Rojas, “Machine Learning as a Tool to Combat Ransomware in Resource-Constrained Business Environment” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151253

@article{Castro2024,
title = {Machine Learning as a Tool to Combat Ransomware in Resource-Constrained Business Environment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151253},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151253},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Luis Jesús Romero Castro and Piero Alexander Cruz Aquino and Fidel Eugenio Garcia Rojas}
}