  Identifying threats in a network traffic flow which is encrypted is uniquely
challenging. On one hand it is extremely difficult to simply decrypt the
traffic due to modern encryption algorithms. On the other hand, passing such an
encrypted stream through pattern matching algorithms is useless because
encryption ensures there aren't any. Moreover, evaluating such models is also
difficult due to lack of labeled benign and malware datasets. Other approaches
have tried to tackle this problem by employing observable meta-data gathered
from the flow. We try to augment this approach by extending it to a
semi-supervised malware classification pipeline using these observable
meta-data. To this end, we explore and test different kind of clustering
approaches which make use of unique and diverse set of features extracted from
this observable meta-data. We also, propose an automated packet data-labeling
pipeline to generate ground-truth data which can serve as a base-line to
evaluate the classifiers mentioned above in particular, or any other detection
model in general.
