Real-time detection of anomalies in streaming data is receiving increasing
attention as it allows us to raise alerts, predict faults, and detect
intrusions or threats across industries. Yet, little attention has been given
to compare the effectiveness and efficiency of anomaly detectors for streaming
data (i.e., of online algorithms). In this paper, we present a qualitative,
synthetic overview of major online detectors from different algorithmic
families (i.e., distance, density, tree or projection-based) and highlight
their main ideas for constructing, updating and testing detection models. Then,
we provide a thorough analysis of the results of a quantitative experimental
evaluation of online detection algorithms along with their offline
counterparts. The behavior of the detectors is correlated with the
characteristics of different datasets (i.e., meta-features), thereby providing
a meta-level analysis of their performance. Our study addresses several missing
insights from the literature such as (a) how reliable are detectors against a
random classifier and what dataset characteristics make them perform randomly;
(b) to what extent online detectors approximate the performance of offline
counterparts; (c) which sketch strategy and update primitives of detectors are
best to detect anomalies visible only within a feature subspace of a dataset;
(d) what are the tradeoffs between the effectiveness and the efficiency of
detectors belonging to different algorithmic families; (e) which specific
characteristics of datasets yield an online algorithm to outperform all others.

本文提供了对不同算法家族的主要在线探测器的质量、综合概述，包括构建、更新和测试探测模型的主要思想，并提供了在线检测算法与离线对应算法的结果定量实验评估的彻底分析，以及对数据集（即元特征）的各种特征进行统计分析。