  Botnets are becoming increasingly prevalent as the primary enabling
technology in a variety of malicious campaigns such as email spam, click fraud,
distributed denial-of-service (DDoS) attacks, and cryptocurrency mining. Botnet
technology has continued to evolve rapidly making detection a very challenging
problem. There is a fundamental need for robust detection methods that are
insensitive to characteristics of a specific botnet and are generalizable
across different botnet types. We propose a novel supervised approach to detect
malicious botnet hosts by tracking a host's network activity over time using a
Long Short-Term Memory (LSTM) based neural network architecture. We build a
prototype to demonstrate the feasibility of our approach, evaluate it on the
CTU-13 dataset, and compare our performance against existing detection methods.
We show that our approach results in a more generalizable, botnet-agnostic
detection methodology, is amenable to real-time implementation, and performs
well compared to existing approaches, with an overall accuracy score of 96.2%.
