Klim Kireev (EPFL), Bogdan Kulynych (EPFL), Carmela Troncoso (EPFL)

Many safety-critical applications of machine learning, such as fraud or abuse detection, use data in tabular domains. Adversarial examples can be particularly damaging for these applications. Yet, existing works on adversarial robustness primarily focus on machine-learning models in image and text domains. We argue that, due to the differences between tabular data and images or text, existing threat models are not suitable for tabular domains. These models do not capture that the costs of an attack could be more significant than imperceptibility, or that the adversary could assign different values to the utility obtained from deploying different adversarial examples. We demonstrate that, due to these differences, the attack and defense methods used for images and text cannot be directly applied to tabular settings. We address these issues by proposing new cost and utility-aware threat models that are tailored to the adversarial capabilities and constraints of attackers targeting tabular domains. We introduce a framework that enables us to design attack and defense mechanisms that result in models protected against cost or utility-aware adversaries, for example, adversaries constrained by a certain financial budget. We show that our approach is effective on three datasets corresponding to applications for which adversarial examples can have economic and social implications.

View More Papers

CANtropy: Time Series Feature Extraction-Based Intrusion Detection Systems for...

Md Hasan Shahriar, Wenjing Lou, Y. Thomas Hou (Virginia Polytechnic Institute and State University)

Read More

Cooperative Perception for Safe Control of Autonomous Vehicles under...

Hongchao Zhang (Washington University in St. Louis), Zhouchi Li (Worcester Polytechnic Institute), Shiyu Cheng (Washington University in St. Louis), Andrew Clark (Washington University in St. Louis)

Read More

QPEP in the Real World: A Testbed for Secure...

Julian Huwyler (ETH Zurich), James Pavur (University of Oxford), Giorgio Tresoldi and Martin Strohmeier (Cyber-Defence Campus) Presenter: Martin Strohmeier

Read More

Browser Permission Mechanisms Demystified

Kazuki Nomoto (Waseda University), Takuya Watanabe (NTT Social Informatics Laboratories), Eitaro Shioji (NTT Social Informatics Laboratories), Mitsuaki Akiyama (NTT Social Informatics Laboratories), Tatsuya Mori (Waseda University/NICT/RIKEN AIP)

Read More