  Recent research shows that colluded malware in different VMs sharing a single
physical host may use a resource as a channel to leak critical information.
Covert channels employ time or storage characteristics to transmit confidential
information to attackers leaving no trail.These channels were not meant for
communication and hence control mechanisms do not exist. This means these
remain undetected by traditional security measures employed in firewalls etc in
a network. The comprehensive survey to address the issue highlights that
accurate methods for fast detection in cloud are very expensive in terms of
storage and processing. The proposed framework builds signature by extracting
features which accurately classify the regular from covert traffic in cloud and
estimates difference in distribution of data under analysis by means of scores.
It then adds context to the signature and finally using machine learning
(Support Vector Machines),a model is built and trained for deploying in cloud.
The results show that the framework proposed is high in accuracy while being
low cost and robust as it is tested after adding noise which is likely to exist
in public cloud environments.
