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Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow by Maurras Ulbricht Togbe; Mariam Barry; Aliou Boly; Yousra Chabchoub; Raja Chiky; Jacob Montiel; Vinh-Thuy Tran is a book available to read on EtoBox.

What is Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow about?

Detecting anomalies in streaming data is an important issue in a variety of real-word applications as it provides some critical information, e.g., Cyber security attacks, Fraud detection or others realtime applications. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based. In this paper, we present a quick survey of the existing anomaly detection methods for data streams. We focus on Isolation Forest (iForest), a stateof-the-art method for anomaly detection. We provide the implementation of IForestASD, a variant of iForest for data streams. This implementation is built on top of scikit-multiflow, an open source machine learning framework for data streams. In fact, few anomalies detection methods are provided in the well-known data streams mining frameworks such as MOA or StreamDM. Hence, we extend scikitmultiflow providing an additional tool. We performed experiments on 3 real-world data sets to evaluate predictive performance and resource consumption (memory and time) of IForestASD and compare it with a well known and state-of-the-art anomaly detection algorithm for data streams called Half-Space Trees.

Author

Maurras Ulbricht Togbe; Mariam Barry; Aliou Boly; Yousra Chabchoub; Raja Chiky; Jacob Montiel; Vinh-Thuy Tran

Publisher

Springer International Publishing : Imprint: Springer

Published

2020

Language

EN

ISBN

9783030588137

Subjects

Engineering, Computer Science, Stem