  We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator
to generate Metamorphic malware), a novel system that uses adversarial deep
reinforcement learning to obfuscate malware at the op-code level for the
enhancement of IDS. The ultimate goal of DOOM is not to give a potent weapon in
the hands of cyber-attackers, but to create defensive-mechanisms against
advanced zero-day attacks. Experimental results indicate that the obfuscated
malware created by DOOM could effectively mimic multiple-simultaneous zero-day
attacks. To the best of our knowledge, DOOM is the first system that could
generate obfuscated malware detailed to individual op-code level. DOOM is also
the first-ever system to use efficient continuous action control based deep
reinforcement learning in the area of malware generation and defense.
Experimental results indicate that over 67% of the metamorphic malware
generated by DOOM could easily evade detection from even the most potent IDS.
This achievement gains significance, as with this, even IDS augment with
advanced routing sub-system can be easily evaded by the malware generated by
DOOM.
