ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912269481279488 |
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| author | Kocal, Cagla Ipek Gungor, Onat Tartz, Aaron Rosing, Tajana Aksanli, Baris |
| author_facet | Kocal, Cagla Ipek Gungor, Onat Tartz, Aaron Rosing, Tajana Aksanli, Baris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07882 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks Kocal, Cagla Ipek Gungor, Onat Tartz, Aaron Rosing, Tajana Aksanli, Baris Machine Learning Cryptography and Security 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. |
| title | ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2503.07882 |