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

AI assisted Malware Analysis: A Course for Next Generation Cybersecurity Workforce