Protecting Classifiers From Attacks

Abstract

In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates to pursue certain goals. Such problems pertain to the field of adversarial machine learning and have been mainly dealt with, perhaps implicitly, through game-theoretic ideas with strong underlying common knowledge assumptions. These are not realistic in numerous application domains in relation to security and business competition. We present an alternative Bayesian decision theoretic framework that accounts for the uncertainty about the attacker's behavior using adversarial risk analysis concepts. In doing so, we also present core ideas in adversarial machine learning to a statistical audience. A key ingredient in our framework is the ability to sample from the distribution of originating instances given the, possibly attacked, observed ones. We propose an initial procedure based on approximate Bayesian computation usable during operations; within it, we simulate the attacker's problem taking into account our uncertainty about his elements. Large-scale problems require an alternative scalable approach implementable during the training stage. Globally, we are able to robustify statistical classification algorithms against malicious attacks.

Published in Statistical Science: https://projecteuclid.org/journals/statistical-science/volume-39/issue-3/Protecting-Classifiers-from-Attacks/10.1214/24-STS922.full

References in corpus (11)

• One pixel attack for fooling deep neural networks
• Semi-Supervised Learning with Deep Generative Models
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• A Complete Recipe for Stochastic Gradient MCMC
• Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
• Adversarial classification: An adversarial risk analysis approach
• Stochastic Gradient MCMC with Repulsive Forces
• Approximating Bayes in the 21st Century
• Augmented Probability Simulation Methods for Sequential Games