The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.

中文翻译：

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静态网络中异常检测的谱方法的统计评估

近年来，网络异常检测的话题引起了很多关注，尤其是随着连接设备和社交网络的兴起。异常检测的应用范围很广，从在反恐工作中检测恐怖分子细胞到识别核糖核酸转录过程中的意外突变。相应地，已经引入了许多用于异常检测的算法技术。然而，迄今为止，从统计角度评估这些算法的工作很少。这项工作旨在通过对一套用于网络异常检测的流行光谱方法进行统计评估来解决文献中的这一空白。我们对这些算法的统计特性的调查揭示了几个重要和关键的缺陷，我们通过方法改进来解决这些缺陷。此外，我们使用模拟网络对这些算法进行了性能评估，并将方法从二进制扩展到计数网络。