A Comparison of Graph Neural Networks for Malware Classification. (arXiv:2303.12812v1 [cs.LG])

Managing the threat posed by malware requires accurate detection and
classification techniques. Traditional detection strategies, such as signature
scanning, rely on manual analysis of malware to extract relevant features,
which is labor intensive and requires expert knowledge. Function call graphs
consist of a set of program functions and their inter-procedural calls,
providing a rich source of information that can be leveraged to classify
malware without the labor intensive feature extraction step of traditional
techniques. In this research, we treat malware …

analysis call classification detection expert extract features function functions graphs information knowledge labor malware malware classification networks neural networks program scanning signature techniques threat