  Banking Trojans, botnets are primary drivers of financially-motivated
cybercrime. In this paper, we first analyzed how an APT-based banking botnet
works step by step through the whole lifecycle. Specifically, we present a
multi-stage system that detects malicious banking botnet activities which
potentially target the organizations. The system leverages Cyber Data Lake as
well as multiple artificial intelligence techniques at different stages. The
evaluation results using public datasets showed that Deep Learning based
detections were highly successful compared with baseline models.
