pyISC: A Bayesian Anomaly Detection Framework for Python

Emruli, Blerim (RISE SICS Västerås) | Olsson, Tomas (RISE SICS Västerås) | Holst, Anders (RISE SICS Västerås)

AAAI Conferences 

The pyISC is a Python API and extension to the C++ based Incremental Stream Clustering (ISC) anomaly detection and classification framework. The framework is based on parametric Bayesian statistical inference using the Bayesian Principal Anomaly (BPA), which enables to combine the output from several probability distributions. pyISC is designed to be easy to use and integrated with other Python libraries, specifically those used for data science. In this paper, we show how to use the framework and we also compare its performance to other well-known methods on 22 real-world datasets. The simulation results show that the performance of pyISC is comparable to the other methods. pyISC is part of the Stream toolbox developed within the STREAM project.