A Survey on Cross-Architectural IoT Malware Threat Hunting
Anandharaju Durai Raju Ibrahim Abualhaol Ronnie Salvador Giagone Yang Zhou Shengqiang Huang
In recent years, the increase in non-Windows malware threats had turned the
focus of the cybersecurity community. Research works on hunting Windows
PE-based malwares are maturing, whereas the developments on Linux malware
threat hunting are relatively scarce. With the advent of the Internet of Things
(IoT) era, smart devices that are getting integrated into human life have
become a hackers highway for their malicious activities. The IoT devices employ
various Unix-based architectures that follow ELF (Executable and Linkable
Format) as their standard binary file specification. This study aims at
providing a comprehensive survey on the latest developments in
cross-architectural IoT malware detection and classification approaches. Aided
by a modern taxonomy, we discuss the feature representations, feature
extraction techniques, and machine learning models employed in the surveyed
works. We further provide more insights on the practical challenges involved in
cross-architectural IoT malware threat hunting and discuss various avenues to
instill potential future research.