ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks

• URL: http://arxiv.org/abs/2503.07882v1
• Date: Mon, 10 Mar 2025 21:55:50 GMT
• Title: ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
• Authors: Cagla Ipek Kocal, Onat Gungor, Aaron Tartz, Tajana Rosing, Baris Aksanli,
• Abstract summary: We introduce ReLATE, a framework that identifies robust learners based on dataset similarity.<n>ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios.<n>It reduces computational overhead by an average of 81.2%, enhancing adversarial resilience and streamlining robust model selection.
• Score: 6.20056740621519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust learners based on dataset similarity, reduces computational overhead, and enhances resilience. ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios, capturing model performance. ReLATE identifies the most analogous dataset to a given target using a similarity metric, then applies the optimal model from the most similar dataset. ReLATE reduces computational overhead by an average of 81.2%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 4.2% of Oracle.

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