通过联合信息保留的对抗鲁棒多变量时间序列异常检测
Adversarial-Robust Multivariate Time-Series Anomaly Detection via Joint Information Retention
AI总结：
本文提出ARTA框架，通过对抗训练提升时间序列异常检测的鲁棒性，实验表明其在多种数据集上表现优异，噪声增加时降解更温和。
摘要：
时间序列异常检测（TSAD）是监控复杂系统的关键组件，但现代深度学习检测器对局部输入损坏和结构噪声敏感。我们提出ARTA（通过联合信息保留的对抗鲁棒多变量时间序列异常检测），一种联合训练框架，通过原理化的min-max优化目标提高检测器鲁棒性。ARTA包含一个异常检测器和一个稀疏约束掩码生成器，同时训练。生成器识别最小的任务相关时间扰动，以最大化检测器的异常分数，而检测器被优化以在这些结构扰动下保持稳定。生成的掩码表征检测器对对抗性时间损坏的敏感性，并可作为检测器决策的解释信号。这种对抗训练策略暴露了脆弱的决策路径，促使检测器依赖分布和稳定的时空模式，而非虚假的局部特征。我们在TSB-AD基准上进行了广泛实验，证明ARTA在多种数据集上一致提升异常检测性能，并在噪声增加时比最先进的基线表现更稳健。
英文摘要：
Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via joint information retention), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, task-relevant temporal perturbations that maximally increase the detector's anomaly score, while the detector is optimized to remain stable under these structured perturbations. The resulting masks characterize the detector's sensitivity to adversarial temporal corruptions and can serve as explanatory signals for the detector's decisions. This adversarial training strategy exposes brittle decision pathways and encourages the detector to rely on distributed and stable temporal patterns rather than spurious localized artifacts. We conduct extensive experiments on the TSB-AD benchmark, demonstrating that ARTA consistently improves anomaly detection performance across diverse datasets and exhibits significantly more graceful degradation under increasing noise levels compared to state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
