Transcend: Detecting Concept Drift in Malware Classification Models

By Roberto Jordaney, K. Sharad, Santanu Kumar Dash, Zhi Wang, D. Papini, Ilia Nouretdinov, Lorenzo Cavallaro. 2017

337 citations recorded by the source corpus.

Subfields: Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Privacy & Security

1. Roberto Baldoni
2. Leonardo Aniello
3. Fabio Pierazzi
4. Lorenzo Cavallaro
5. Daniele Ucci
6. Feargus Pendlebury

Citations in the corpus

• TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (2018, methodology): “Malware classifiers are strongly affected by concept drift: as new malware variants and families appear, their performance decays over time [20].”
• Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “Indeed, temporal information is crucial to evaluate malware analysis results [95] and determine whether machine learning models have become obsolete [96, 97].”