Explorer On Robust Malware Classifiers by Verifying Unwanted Behaviours

@inproceedings{Chen2017ExplorerOR,
  title={Explorer On Robust Malware Classifiers by Verifying Unwanted Behaviours},
  author={Wei Chen and David Aspinall and Andrew D. Gordon and Charles E. Sutton and Igor Muttik},
  year={2017}
}
Machine-learning-based Android malware classifiers perform badly on the detection of new malware, in particular, when they take API calls and permissions as input features, which are the best performing features known so far. This is mainly because signature-based features are very sensitive to the training data and cannot capture general behaviours of identified malware. To improve the robustness of classifiers, we study the problem of learning and verifying unwanted behaviours abstracted as… CONTINUE READING