  Defending against botnets has always been a cat and mouse game.
Cyber-security researchers and government agencies attempt to detect and take
down botnets by playing the role of the cat. In this context, a lot of work has
been done towards reverse engineering certain variants of malware families as
well as understanding the network protocols of botnets to identify their
weaknesses (if any) and exploit them. While this is necessary, such an approach
offers the botmasters the ability to quickly counteract the defenders by simply
performing small changes in their arsenals.
  We attempt a different approach by actually taking the role of the Botmaster,
to eventually anticipate his behavior. That said, in this paper, we present a
novel computational trust mechanism for fully distributed botnets that allows
for a resilient and stealthy management of the infected machines (zombies). We
exploit the highly researched area of computational trust to create an
autonomous mechanism that ensures the avoidance of common botnet tracking
mechanisms such as sensors and crawlers. In our futuristic botnet, zombies are
both smart and cautious. They are cautious in the sense that they are careful
with whom they communicate with. Moreover, they are smart enough to learn from
their experiences and infer whether their fellow zombies are indeed who they
claim to be and not government agencies' spies. We study different
computational trust models, mainly based on Bayesian inference, to evaluate
their advantages and disadvantages in the context of a distributed botnet.
Furthermore, we show, via our experimental results, that our approach is
significantly stronger than any technique that has been seen in botnets to
date.
