  This paper delves into the dynamic landscape of computer security, where
malware poses a paramount threat. Our focus is a riveting exploration of the
recent and promising hardware-based malware detection approaches. Leveraging
hardware performance counters and machine learning prowess, hardware-based
malware detection approaches bring forth compelling advantages such as
real-time detection, resilience to code variations, minimal performance
overhead, protection disablement fortitude, and cost-effectiveness. Navigating
through a generic hardware-based detection framework, we meticulously analyze
the approach, unraveling the most common methods, algorithms, tools, and
datasets that shape its contours. This survey is not only a resource for
seasoned experts but also an inviting starting point for those venturing into
the field of malware detection. However, challenges emerge in detecting malware
based on hardware events. We struggle with the imperative of accuracy
improvements and strategies to address the remaining classification errors. The
discussion extends to crafting mixed hardware and software approaches for
collaborative efficacy, essential enhancements in hardware monitoring units,
and a better understanding of the correlation between hardware events and
malware applications.
