Evaluating the Capacity of Machine Learning Models to Quell Cybersecurity Threats - Abiodun Kazeem Oniyide; Kenneth O. Ogirri; Chinonso Francis Nkeoma Ubbaonu (International Journal of Scientific Research in Science and Technology, 2024)
There are increasing threats to individuals, digital resources, critical infrastructure and phenomena on the cyberspace. The threats raise national and global concerns. This study explores the capacity of machine learning (ML) models to effectively quell the rising cybersecurity threats in contemporary times. It relied on secondary data and employed qualitative method. The data, drawn from several major repositories, were analyzed qualitatively. Applying exclusion and inclusion criteria, some of the gathered data were exclude, while others were include in the study. The analysis demonstrates that ML models are capable of quelling cybersecurity threats by detecting, predicting, resisting, preventing, automating, and optimizing cybersecurity processes and operations, thereby safeguarding the cyberspace at an appreciable extent. By undertaking these advanced tasks that are beyond human capacity, ML models are highly advantageous amidst the identified ethical concerns associated with them. The study argues that although machine learning models have their lapses, they are more capable of quelling cybersecurity threats than human beings could do. Therefore, it calls on stakeholders to significantly leverage ML models in combination with other technology-driven and conventional measures for combating cybersecurity threats in various settings.
Keywords: Machine learning models; Cybersecurity threats; Quelling; Capacity; AI