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

TL;DR

ReLATE+ is a unified framework for detecting, classifying adversarial attacks, and adaptively selecting models in time-series classification, significantly reducing computational costs while maintaining high performance across diverse datasets.

Contribution

The paper introduces ReLATE+, a novel framework that combines adversarial attack detection, classification, and dataset-aware model selection to improve efficiency and robustness in time-series classification.

Findings

Reduces computational overhead by 77.68% on average.

Maintains performance within 2.02% of Oracle.

Generalizes well across different domains.

Abstract

Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge due to the high complexity of model architectures and the large volume of sequential data that must be processed in real time. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. To address this challenge, we propose ReLATE+, a comprehensive framework that detects and classifies adversarial attacks, adaptively selects deep learning models based on dataset-level similarity, and thus substantially reduces retraining costs relative to conventional methods that do not leverage prior knowledge, while maintaining strong performance. ReLATE+ first checks whether the incoming data is adversarial and, if so, classifies the attack type, using this insight to identify a similar dataset from a repository and enable the reuse of the best-performing associated model. 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.