Improving Malware Classification with Uncertainty Estimation in Shifting Datasets
Published:Dec 20, 2025 20:17
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
Reference
“The research focuses on Windows PE malware classification.”