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 attackers constantly modify existing malware. In this chapter, we analyze two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means). For comparison
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Christofer Washington Berruz Chungata →
“Concept Drift Detection and Adaptive Retraining of Malware Classification Models”
- LinkedLinked via arxiv author · 85%Martin Jurecek →
“Concept Drift Detection and Adaptive Retraining of Malware Classification Models”
- LinkedLinked via arxiv author · 85%Katerina Potika →
“Concept Drift Detection and Adaptive Retraining of Malware Classification Models”
- LinkedLinked via arxiv author · 85%William B. Andreopoulos →
“Concept Drift Detection and Adaptive Retraining of Malware Classification Models”
- LinkedLinked via arxiv author · 85%Mark Stamp →
“Concept Drift Detection and Adaptive Retraining of Malware Classification Models”