Transcend: Detecting Concept Drift in Malware Classification Models

@inproceedings{Jordaney2017TranscendDC,
  title={Transcend: Detecting Concept Drift in Malware Classification Models},
  author={Roberto Jordaney and Kumar Sharad and Santanu Kumar Dash and Zhi Wang and Davide Papini and Ilia Nouretdinov and Lorenzo Cavallaro},
  booktitle={USENIX Security Symposium},
  year={2017}
}
Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming… CONTINUE READING

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