ABSTRACT :  A large collection of software and hardware sensors exist for monitoring network traffic at different granularity and alerting when suspicious traffic is encountered. The sensors utilize large and diverse rule-sets to detect malicious network traffic patterns. The data generated by these sensors can be utilized to provide a holistic assessment and reason about network threat patterns. We propose an analytic pipeline which applies graph theoretic and machine learning methods to achieve this. The proposed analytics pipeline allows a holistic assessment of network traffic patterns at custom temporal granularity. Further, temporal coccurrence of host interactions and associativity can help discover possible collusion and attack campaign signatures. This automated workflow is extendable and customizable by adding new computation blocks and an interactive, human-in-the-loop experience.