  Antivirus developers are increasingly embracing machine learning as a key
component of malware defense. While machine learning achieves cutting-edge
outcomes in many fields, it also has weaknesses that are exploited by several
adversarial attack techniques. Many authors have presented both white-box and
black-box generators of adversarial malware examples capable of bypassing
malware detectors with varying success. We propose to combine contemporary
generators in order to increase their potential. Combining different generators
can create more sophisticated adversarial examples that are more likely to
evade anti-malware tools. We demonstrated this technique on five well-known
generators and recorded promising results. The best-performing combination of
AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9%
against top-tier antivirus products. This represents an average improvement of
more than 36% and 627% over using only the AMG-random and MAB-Malware
generators, respectively. The generator that benefited the most from having
another generator follow its procedure was the FGSM injection attack, which
improved the evasion rate on average between 91.97% and 1,304.73%, depending on
the second generator used. These results demonstrate that combining different
generators can significantly improve their effectiveness against leading
antivirus programs.
