  The use of Artificial Intelligence (AI) and Machine Learning (ML) to solve
cybersecurity problems has been gaining traction within industry and academia,
in part as a response to widespread malware attacks on critical systems, such
as cloud infrastructures, government offices or hospitals, and the vast amounts
of data they generate. AI- and ML-assisted cybersecurity offers data-driven
automation that could enable security systems to identify and respond to cyber
threats in real time. However, there is currently a shortfall of professionals
trained in AI and ML for cybersecurity. Here we address the shortfall by
developing lab-intensive modules that enable undergraduate and graduate
students to gain fundamental and advanced knowledge in applying AI and ML
techniques to real-world datasets to learn about Cyber Threat Intelligence
(CTI), malware analysis, and classification, among other important topics in
cybersecurity.
  Here we describe six self-contained and adaptive modules in "AI-assisted
Malware Analysis." Topics include: (1) CTI and malware attack stages, (2)
malware knowledge representation and CTI sharing, (3) malware data collection
and feature identification, (4) AI-assisted malware detection, (5) malware
classification and attribution, and (6) advanced malware research topics and
case studies such as adversarial learning and Advanced Persistent Threat (APT)
detection.
