Convolutional Neural Networks for Malware Classification

@inproceedings{Gibert2016ConvolutionalNN,
  title={Convolutional Neural Networks for Malware Classification},
  author={Daniel Gibert and Javier B{\'e}jar},
  year={2016}
}
According to AV vendors malicious software has been growing exponentially last years. One of the main reasons for these high volumes is that in order to evade detection, malware authors started using polymorphic and metamorphic techniques. As a result, traditional signature-based approaches to detect malware are being insufficient against new malware and the categorization of malware samples had become essential to know the basis of the behavior of malware and to fight back cybercriminals… CONTINUE READING