Adversarial Training of Linear Models under Stealthy Attacks
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Computer Science > Machine Learning
Title:Adversarial Training of Linear Models under Stealthy Attacks
Abstract:Predictive models are widely used in many fields, but are vulnerable to false data injection attacks. To address this, detection schemes and adversarial training have been proposed, but such approaches lack guarantees against stealthy attacks. We therefore propose a detector-based switched model, in which optimal attack strategies are stealthy. For linear prediction models, we derive a convex formulation of the resulting adversarial risk. The model incorporates protected features and introduces a hyperparameter modelling attack probability, enabling an explicit performance trade-off between clean and attacked data regimes. Numerical simulations on real and synthetic data show improved performance on partially attacked data, even for misspecified attack probabilities.
| Comments: | This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR); Systems and Control (eess.SY) |
| Cite as: | arXiv:2608.25681 [cs.LG] |
| (or arXiv:2608.25681v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.25681
arXiv-issued DOI via DataCite (pending registration)
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