Evaluation of Machine Learning Algorithms for Predicting Cybersecurity Incidents
Aniruddha Chatterjee · 2025
This research examines the utilization of machine learning to proactively forecast cybersecurity problems, hence reducing the likelihood of substantial system interruptions. Utilizing the public GUIDE dataset, we intend to create a machine learning model that can analyze historical event data and discern trends that signal emerging hazards. The model will be developed to enhance current security measures by delivering early alerts of possible problems, facilitating prompt intervention, and averting system failures. This research's conclusions aim to improve cybersecurity measures and mitigate the financial and operational consequences of intrusions.