FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Abstract

Supervised machine learning classifiers have been widely used for attack detection, but their training requires abundant high-quality labels. Unfortunately, high-quality labels are difficult to obtain in practice due to the high cost of data labeling and the constant evolution of attackers. Without such labels, it is challenging to train and deploy targeted countermeasures.