  Ransomware has become a significant global threat with the
ransomware-as-a-service model enabling easy availability and deployment, and
the potential for high revenues creating a viable criminal business model.
Individuals, private companies or public service providers e.g. healthcare or
utilities companies can all become victims of ransomware attacks and
consequently suffer severe disruption and financial loss. Although machine
learning algorithms are already being used to detect ransomware, variants are
being developed to specifically evade detection when using dynamic machine
learning techniques. In this paper, we introduce NetConverse, a machine
learning analysis of Windows ransomware network traffic to achieve a high,
consistent detection rate. Using a dataset created from conversation-based
network traffic features we achieved a true positive detection rate of 97.1%
using the Decision Tree (J48) classifier.
