The recent increases in Internet use and the number of malicious attacks are helping attackers generate malware variants on various operating systems (OS). This has led to an increase in the volume, type, and sophistication of malware. As a result, there is a critical need for an improved platform-agnostic malware analysis system that can stop the rapid expansion of malicious activities and minimize the use of various cybersecurity tools for different operating systems. In this chapter, we propose PAMDAC, a system for platform-agnostic malware detection and classification. PAMDAC uses platform-agnostic features that are derived from the analyzed malware sample and do not depend on the knowledge of the platform which the malware targets. In this comprehensive study, we look at the performance of different binary features for malware detection and classification on various platform (Android, Linux, and Mac OS) datasets. Experimental results are quite promising with highly accurate classification results on various malware datasets. Finally, we perform a case study to look at the effectiveness of using such platform-agnostic features on the amalgamation of the aforementioned datasets for malware detection.