Abstract:
To prevent citizens from accessing unfiltered information over the internet,
repressive governments usually deploy national level censorship. To counter
these censors, a number of systems have been introduced that make use of
various allowed applications as covert channels and tunneling data through
them. However, a drawback of these covert channels is their failure to maintain un-observability. The censors these days are capable enough to identify
covert traffic amongst the stream of regular traffic. With censors getting
more rigorous, some new ways of hiding blocked content in legitimate data
streams have been introduced. These systems have very closely fooled the
similarity based censors. However, Machine learning systems were still unbeaten. A recent study showed that some state-of-the-art Machine learning
systems have been able to identify covert traffic, deeming the censor ship resistant models useless. In this thesis, we have used the strongest known
Censorship-resistant system till date, to show that the system can be tweaked
to defeat a machine learning based anomaly detection system. We have made
use of adversarial machine learning techniques to beat some well-known classification techniques. Our results show that Adversarial examples can not
only be used to hide blocked content in an application data stream but can
also remain undetected.
