ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification

Abstract

Minimizing computational overhead in healthcare time-series classification remains a critical challenge, particularly for deep learning models operating on high-dimensional sequential data under resource and latency constraints. This challenge is further exacerbated by adversarial attacks, which introduce evolving threats and necessitate robust yet efficient mechanisms for maintaining reliable model performance—a requirement of paramount importance in healthcare, where wearable and smart devices have brought continuous monitoring outside clinical settings and model failures can directly compromise patient safety. In this paper, we propose ReLATE+, a unified framework for adversarially robust and computationally efficient time-series classification. ReLATE+ integrates three key capabilities: (i) detection and classification of adversarial inputs, (ii) dataset-level similarity analysis, and (iii) adaptive model selection. Upon receiving new data, the framework first determines whether the input is adversarial and identifies the attack type. It then leverages this information to retrieve a similar dataset from a repository and identify the corresponding high-performing models and trains only a small set of selected candidates instead of exhaustively retraining all models. This approach ensures strong performance while reducing the need for retraining, and it generalizes well across different domains with varying data distributions and feature spaces. Experiments show that ReLATE+ reduces computational overhead by an average of 77.68%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 2.02% of Oracle.