Support vector machines resilient against training data integrity attacks

Abstract

Support Vector Machines (SVMs) are vulnerable to integrity attacks, where malicious attackers distort the training data in order to compromise the decision boundary of the learned model. With increasing real-world applications of SVMs, malicious data that is classified as innocuous may have harmful consequences. This paper presents a novel framework that utilizes adversarial learning, nonlinear data projections, and game theory to improve the resilience of SVMs against such training-data-integrity attacks. The proposed approach introduces a layer of uncertainty through the use of random projections on top of the learners, making it challenging for the adversary to guess the specific configur..