Concept Drift Detection and Adaptive Retraining of Malware Classification Models

Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly susceptible to performance degradation caused by concept drift, as...

Attack-Defense Trees with Offensive and Defensive Attributes (With Appendix)

Effective risk management in cybersecurity requires a thorough understanding of the interplay between attacker capabilities and defense strategies. Attack-Defense Trees ADTs are a commonly used methodology for representing this interplay; however, previous work in this domain has only focused on...