Fast & Furious: Modelling Malware Detection as Evolving Data Streams. (arXiv:2205.12311v3 [cs.CR] UPDATED)

Malware is a major threat to computer systems and imposes many challenges to
cyber security. Targeted threats, such as ransomware, cause millions of dollars
in losses every year. The constant increase of malware infections has been
motivating popular antiviruses (AVs) to develop dedicated detection strategies,
which include meticulously crafted machine learning (ML) pipelines. However,
malware developers unceasingly change their samples' features to bypass
detection. This constant evolution of malware samples causes changes to the
data distribution (i.e., concept drifts) that …

arxiv data data streams detection malware modelling