FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data
Junjie Liang, Wenbo Guo, Tongbo Luo, Vasant Honavar, Gang Wang, Xinyu Xing
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.
Topics & Concepts
CategorizationComputer scienceQuality (philosophy)Artificial intelligencePhilosophyEpistemologyAdvanced Malware Detection TechniquesNetwork Security and Intrusion DetectionAdversarial Robustness in Machine Learning