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.

在需要高可靠性的领域（如网络安全）中，机器学习模型被广泛使用。然而，这些模型仍然容易受到各种攻击的影响，其中敌对的标签翻转攻击构成了重大威胁。本文对此表示担忧，因为这些攻击可以将高度倾斜的数据集伪装成一个容易解决的分类问题，常常误导机器学习从业者降低防御措施并错误地估计潜在风险。我们提出FALFA（快速敌对标签翻转攻击）来展示这种风险是敌对目标的一部分，它是一种新颖高效的用于生成敌对标签的攻击方法。FALFA基于转换敌对目标，并利用线性规划减少计算复杂性。通过使用十个真实世界的表格数据集，我们展示了FALFA具有卓越的攻击潜力，强调了对这种威胁的强大防御的必要性。

基于表格数据的快速对抗标签翻转攻击