Fast Adversarial Label-Flipping Attack on Tabular Data
Xinglong Chang Gillian Dobbie Jörg Wicker
Machine learning models are increasingly used in fields that require high
reliability such as cybersecurity. However, these models remain vulnerable to
various attacks, among which the adversarial label-flipping attack poses
significant threats. In label-flipping attacks, the adversary maliciously flips
a portion of training labels to compromise the machine learning model. This
paper raises significant concerns as these attacks can camouflage a highly
skewed dataset as an easily solvable classification problem, often misleading
machine learning practitioners into lower defenses and miscalculations of
potential risks. This concern amplifies in tabular data settings, where
identifying true labels requires expertise, allowing malicious label-flipping
attacks to easily slip under the radar. To demonstrate this risk is inherited
in the adversary's objective, we propose FALFA (Fast Adversarial Label-Flipping
Attack), a novel efficient attack for crafting adversarial labels. FALFA is
based on transforming the adversary's objective and employs linear programming
to reduce computational complexity. Using ten real-world tabular datasets, we
demonstrate FALFA's superior attack potential, highlighting the need for robust
defenses against such threats.