Improving Malware Classification with Uncertainty Estimation in Shifting Datasets

This research explores a crucial area of cybersecurity, addressing the challenge of accurate malware classification, particularly when datasets evolve. The focus on uncertainty estimation is a valuable approach for improving the reliability and robustness of machine learning models in dynamic environments.