Year: 2024
Type: conference-paper
Abstract: Many domains now leverage the benefits of Machine Learning (ML), which promises solutions that can autonomously learn to solve complex tasks by training over some data. Unfortunately, in cyberthreat detection, high-quality data is hard to come by. Moreover, for some specific applications of ML, such data must be labeled by human operators. Many works "assume" that labeling is tough/challenging/costly in cyberthreat detection, thereby proposing solutions to address such a hurdle. Yet, we found no work that specifically addresses the process of labeling from the viewpoint of ML security practitioners. This is a problem: to this date, it is still mostly unknown how labeling is done in practice---thereby preventing one from pinpointing "what is needed" in the real world.
Institution: University of Liechtenstein
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4398186358