ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
This work addresses computational efficiency and adversarial resilience for time-series classification, offering a domain-specific incremental improvement.
The paper tackles the problem of minimizing computational overhead and ensuring robust performance in time-series classification under adversarial attacks by introducing ReLATE, a framework that selects resilient learners based on dataset similarity, achieving an average 81.2% reduction in computational overhead while maintaining performance within 4.2% of an Oracle.
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.