Self-Supervised Vision Transformers for Malware Detection. (arXiv:2208.07049v1 [cs.CR])

Malware detection plays a crucial role in cyber-security with the increase in
malware growth and advancements in cyber-attacks. Previously unseen malware
which is not determined by security vendors are often used in these attacks and
it is becoming inevitable to find a solution that can self-learn from unlabeled
sample data. This paper presents SHERLOCK, a self-supervision based deep
learning model to detect malware based on the Vision Transformer (ViT)
architecture. SHERLOCK is a novel malware detection method which learns unique …

arxiv detection malware transformers vision