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The aim of this systematic literature review (SLR) is to provide a comprehensive overview of the current state of Windows malware detection techniques, research issues, and future directions. The SLR was conducted by analyzing scientific literature on Windows malware detection based on executable files (.EXE file format) published between 2009 and 2022. The study presents new insights into the categorization of malware detection techniques based on datasets, features, machine learning and deep learning algorithms. It identifies ten experimental biases that could impact the performance of malware detection techniques. We provide insights on performance evaluation metrics and discuss several research issues that impede the effectiveness of existing techniques. The study also provides recommendations for future research directions and is a valuable resource for researchers and practitioners working in …