异常值检测是数据挖掘中数据准备的重要环节 ,也 是学界探讨和研究的内容 。目前主要有3种策略 : (1)统计法:对样本总体分布作出假设的基础上,构造如四分位点、标准差等统计量进行检测,主要适用于单属性值的情况。 (2)距离法:将两个样本视为维空间的两点,计算两点间的Minkowski,Chebyshev或Mahalanobis距离来度量,此方法 能够应用 于多元 数值 ,但 没有综合考虑总体分布的因素,导致 太依赖于参数的选择。 (3)分类法: 建立分类模 型判断数据类别,以认定其是否与总体偏 离 ,一般 需要有大量样本集 以训练分类模型,并且此方法判断的颗粒度较大,相对于精细的数据要求显得误判率较高。

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A Survey on GANs for Anomaly Detection

异常检测是当前研究领域面临的一个重要问题。检测和正确分类看不见的异常样本是一个具有挑战性的问题,多年来已经有很多方式在解决。

生成式对抗网络(GANs)和对抗训练过程最近已被用于面对这一任务,并产生了显著的结果。在本文中,我们调研了主要GAN-based异常检测方法,突出优点和缺点。我们的贡献是主要的实证验证GAN异常检测模型,在不同的数据集实验结果的增加和公众发布一个完整的开源工具箱使用GAN进行异常检测。

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Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MSTREAM which can detect unusual group anomalies as they occur, in a dynamic manner. MSTREAM has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MSTREAM is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.

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Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MSTREAM which can detect unusual group anomalies as they occur, in a dynamic manner. MSTREAM has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MSTREAM is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.

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