论文标题
从复杂不断发展的数据流中的在线深度异常检测的自适应模型汇总
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream
论文作者
论文摘要
来自数据流的在线异常检测对于许多应用程序的安全性至关重要，但由于来自IoT设备和基于云的基础架构的复杂且不断发展的数据流而面临严重的挑战。不幸的是，对于这些挑战而言，现有方法太短了。在线异常检测方法承担着处理复杂性的负担，而离线深度异常检测方法则遭受了不断发展的数据分布。本文介绍了一个在线深度异常检测的框架Arcus，可以与任何基于自动编码器的深度异常检测方法进行实例化。它使用自适应模型合并方法通过两种新颖的技术来处理复杂而不断发展的数据流：概念驱动的推理和漂移感知的模型池更新；前者通过最适合复杂性的模型组合检测异常，而后者则动态调整模型池以适合不断发展的数据流。在使用十个既具有高维和概念的数据集的综合实验，ARCUS提高了基于最先进的自动编码器方法的流媒体变体的异常检测准确性，又提高了最先进的ART流媒体流媒体异常检测方法的准确性，分别提高了22％和37％。
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures. Unfortunately, existing approaches fall too short for these challenges; online anomaly detection methods bear the burden of handling the complexity while offline deep anomaly detection methods suffer from the evolving data distribution. This paper presents a framework for online deep anomaly detection, ARCUS, which can be instantiated with any autoencoder-based deep anomaly detection methods. It handles the complex and evolving data streams using an adaptive model pooling approach with two novel techniques: concept-driven inference and drift-aware model pool update; the former detects anomalies with a combination of models most appropriate for the complexity, and the latter adapts the model pool dynamically to fit the evolving data streams. In comprehensive experiments with ten data sets which are both high-dimensional and concept-drifted, ARCUS improved the anomaly detection accuracy of the streaming variants of state-of-the-art autoencoder-based methods and that of the state-of-the-art streaming anomaly detection methods by up to 22% and 37%, respectively.
