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

Summary:

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 81.21%, enhancing adversarial resilience and streamlining robust model selection,
all without sacrificing performance, within 4.2% of Oracle.

Author(s):

Cagla Ipek Kocal    
San Diego State University
United States

Onat Gungor    
University of California San Diego
United States

Aaron Tartz    
San Diego State University
United States

Tajana Rosing    
University of California San Diego
United States

Baris Aksanli    
San Diego State University
United States