发表机构
Curtin University; Umeå University（科廷大学; 于默奥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对物联网环境下MLaaS客户端为黑盒的漂移检测难题，提出MPDD模型与APDDM机制，实验显示二者可显著提升检测准确率、降低漏检率。
AI 中文摘要
机器学习即服务（MLaaS）是一种强大的云范式，可在物联网（IoT）环境中支持数据驱动的智能应用，由于成本效益高，已广泛应用于医疗保健、智能家居和工业领域。然而，物联网的动态特性经常改变数据分布，影响MLaaS的稳定性，而MLaaS的定期更新进一步会引发性能漂移。与传统机器学习系统不同，MLaaS客户端作为黑盒用户，无法访问内部数据或参数，这使得漂移检测极具挑战性。为解决该问题，我们提出了一种面向物联网环境的新型MLaaS性能漂移检测框架。该框架首先采用MLaaS提取模型，从输入输出对中学习服务行为并识别影响预测的特征。在此基础上，所提出的MLaaS性能漂移检测（MPDD）模型可联合捕捉输入数据和MLaaS行为的变化。我们进一步设计了自适应时序性能漂移检测机制（APDDM），该机制可根据行为和数据变化动态调整监测频率，从而实现及时的漂移检测以支持有效的服务管理。对真实世界数据集开展的大量实验表明，MPDD相较于基线漂移检测方法可实现最高22%-25%的准确率提升；与固定间隔监测相比，APDDM可提供约4%的平均准确率增益，并将漏检率降低约9%。
英文摘要
Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across healthcare, smart homes, and industry due to its cost-effectiveness. However, the dynamic nature of IoT frequently alters data distributions, affecting MLaaS stability, while periodic MLaaS updates further introduce performance drift. Unlike traditional ML systems, MLaaS clients operate as black-box users without access to internal data or parameters, making drift detection particularly challenging. To address this, we propose a novel MLaaS Performance Drift Detection framework for IoT environments. The framework first employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features. Building on this, the proposed MLaaS Performance Drift Detection (MPDD) model jointly captures variations in input data and MLaaS behavior. We further design an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that dynamically adjusts monitoring frequency based on behavioral and data variations, enabling timely drift detection for effective service management. Extensive experiments on real-world datasets demonstrate that MPDD achieves up to 22-25% accuracy improvement over baseline drift detection methods. APDDM provides an average accuracy gain of approximately 4% and reduces the miss detection rate by around 9% compared to fixed-interval monitoring.
